From 9b0634a486e03490c39c5e5bbbebf1d6fc18f8c2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Af=C3=ABrina=20Skeja?= Date: Tue, 23 Sep 2025 16:14:09 +0200 Subject: [PATCH] Added translation using Weblate (Albanian) --- po/QML-sq.po | 2807 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2807 insertions(+) create mode 100644 po/QML-sq.po diff --git a/po/QML-sq.po b/po/QML-sq.po new file mode 100644 index 00000000..6b8e9f9b --- /dev/null +++ b/po/QML-sq.po @@ -0,0 +1,2807 @@ +msgid "" +msgstr "" +"Last-Translator: Automatically generated\n" +"Language-Team: none\n" +"Language: sq\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Plural-Forms: nplurals=2; plural=n != 1;\n" +"X-Qt-Contexts: true\n" + +msgctxt "Description|" +msgid "Machine Learning" +msgstr "" + +msgctxt "Description|" +msgid "Regression" +msgstr "" + +msgctxt "Description|" +msgid "Boosting" +msgstr "" + +msgctxt "Description|" +msgid "Boosting Regression" +msgstr "" + +msgctxt "Description|" +msgid "K-Nearest Neighbors" +msgstr "" + +msgctxt "Description|" +msgid "K-Nearest Neighbors Regression" +msgstr "" + +msgctxt "Description|" +msgid "Random Forest" +msgstr "" + +msgctxt "Description|" +msgid "Random Forest Regression" +msgstr "" + +msgctxt "Description|" +msgid "Regularized Linear" +msgstr "" + +msgctxt "Description|" +msgid "Regularized Linear Regression" +msgstr "" + +msgctxt "Description|" +msgid "Classification" +msgstr "" + +msgctxt "Description|" +msgid "Boosting Classification" +msgstr "" + +msgctxt "Description|" +msgid "K-Nearest Neighbors Classification" +msgstr "" + +msgctxt "Description|" +msgid "Linear Discriminant" +msgstr "" + +msgctxt "Description|" +msgid "Linear Discriminant Classification" +msgstr "" + +msgctxt "Description|" +msgid "Random Forest Classification" +msgstr "" + +msgctxt "Description|" +msgid "Clustering" +msgstr "" + +msgctxt "Description|" +msgid "Density-Based" +msgstr "" + +msgctxt "Description|" +msgid "Density-Based Clustering" +msgstr "" + +msgctxt "Description|" +msgid "Fuzzy C-Means" +msgstr "" + +msgctxt "Description|" +msgid "Fuzzy C-Means Clustering" +msgstr "" + +msgctxt "Description|" +msgid "Hierarchical" +msgstr "" + +msgctxt "Description|" +msgid "Hierarchical Clustering" +msgstr "" + +msgctxt "Description|" +msgid "Random Forest Clustering" +msgstr "" + +msgctxt "DataSplit|" +msgid "Data Split Preferences" +msgstr "" + +msgctxt "DataSplit|" +msgid "Holdout Test Data" +msgstr "" + +msgctxt "DataSplit|" +msgid "Sample" +msgstr "" + +msgctxt "DataSplit|" +msgid "% of all data" +msgstr "" + +msgctxt "DataSplit|" +msgid "Add generated indicator to data" +msgstr "" + +msgctxt "DataSplit|" +msgid "None" +msgstr "" + +msgctxt "DataSplit|" +msgid "Training and Validation Data" +msgstr "" + +msgctxt "DataSplit|" +msgid "% for validation data" +msgstr "" + +msgctxt "DataSplit|" +msgid "K-fold with" +msgstr "" + +msgctxt "DataSplit|" +msgid "folds" +msgstr "" + +msgctxt "DataSplit|" +msgid "Leave-one-out" +msgstr "" + +msgctxt "mlClassificationBoosting|" +msgid "Tables" +msgstr "" + +msgctxt "mlClassificationBoosting|" +msgid "Plots" +msgstr "" + +msgctxt "mlClassificationBoosting|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClassificationBoosting|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClassificationKnn|" +msgid "Tables" +msgstr "" + +msgctxt "mlClassificationKnn|" +msgid "Plots" +msgstr "" + +msgctxt "mlClassificationKnn|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClassificationKnn|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Tables" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Coefficients" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Prior and posterior probabilities" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Class means training data" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Assumption Checks" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Equality of class means" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Equality of covariance matrices" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Multicollinearity" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Plots" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Linear discriminant matrix" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Densities" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Scatter plots" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClassificationRandomForest|" +msgid "Tables" +msgstr "" + +msgctxt "mlClassificationRandomForest|" +msgid "Plots" +msgstr "" + +msgctxt "mlClassificationRandomForest|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClassificationRandomForest|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Tables" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Plots" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "K-distance plot" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Normal" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Correlated" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Model Optimization" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Fixed" +msgstr "" + +msgctxt "mlClusteringFuzzyCMeans|" +msgid "Tables" +msgstr "" + +msgctxt "mlClusteringFuzzyCMeans|" +msgid "Plots" +msgstr "" + +msgctxt "mlClusteringFuzzyCMeans|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClusteringFuzzyCMeans|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Tables" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Plots" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Dendrogram" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Euclidean" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Average" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Single" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Complete" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Centroid" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Median" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Ward.D" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Ward.D2" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "McQuitty" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Tables" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Plots" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClusteringRandomForest|" +msgid "Tables" +msgstr "" + +msgctxt "mlClusteringRandomForest|" +msgid "Plots" +msgstr "" + +msgctxt "mlClusteringRandomForest|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClusteringRandomForest|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlRegressionBoosting|" +msgid "Tables" +msgstr "" + +msgctxt "mlRegressionBoosting|" +msgid "Plots" +msgstr "" + +msgctxt "mlRegressionBoosting|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlRegressionBoosting|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlRegressionKnn|" +msgid "Tables" +msgstr "" + +msgctxt "mlRegressionKnn|" +msgid "Plots" +msgstr "" + +msgctxt "mlRegressionKnn|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlRegressionKnn|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlRegressionRandomForest|" +msgid "Tables" +msgstr "" + +msgctxt "mlRegressionRandomForest|" +msgid "Plots" +msgstr "" + +msgctxt "mlRegressionRandomForest|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlRegressionRandomForest|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Tables" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Plots" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Variable trace" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Legend" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "λ evaluation" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Elastic net" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Lambda (λ)" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Fixed" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Optimized" +msgstr "" + +msgctxt "Description|" +msgid "" +"Explore the relation between variables using data-driven methods for " +"regression, classification, and clustering" +msgstr "" + +msgctxt "Description|" +msgid "Neural Network" +msgstr "" + +msgctxt "Description|" +msgid "Neural Network Regression" +msgstr "" + +msgctxt "Description|" +msgid "Neural Network Classification" +msgstr "" + +msgctxt "Description|" +msgid "Prediction" +msgstr "" + +msgctxt "ExportResults|" +msgid "Export Results" +msgstr "" + +msgctxt "ExportResults|" +msgid "Add predictions to data" +msgstr "" + +msgctxt "ExportResults|" +msgid "Column name" +msgstr "" + +msgctxt "ExportResults|" +msgid "e.g., predicted" +msgstr "" + +msgctxt "ExportResults|" +msgid "Save as" +msgstr "" + +msgctxt "ExportResults|" +msgid "e.g., location/model.jaspML" +msgstr "" + +msgctxt "ExportResults|" +msgid "Save trained model" +msgstr "" + +msgctxt "mlClassificationNeuralNetwork|" +msgid "Tables" +msgstr "" + +msgctxt "mlClassificationNeuralNetwork|" +msgid "Plots" +msgstr "" + +msgctxt "mlClassificationNeuralNetwork|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClassificationNeuralNetwork|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlRegressionNeuralNetwork|" +msgid "Tables" +msgstr "" + +msgctxt "mlRegressionNeuralNetwork|" +msgid "Plots" +msgstr "" + +msgctxt "mlRegressionNeuralNetwork|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlRegressionNeuralNetwork|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "DataSplit|" +msgid "Column name" +msgstr "" + +msgctxt "DataSplit|" +msgid "Test set indicator" +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Estimation method" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Epsilon neighborhood size" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Min. core points" +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "Distance" +msgstr "" + +msgctxt "mlClusteringFuzzyCMeans|" +msgid "Max. iterations" +msgstr "" + +msgctxt "mlClusteringFuzzyCMeans|" +msgid "Fuzziness parameter" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Distance" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Pearson" +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "Linkage" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Max. iterations" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Random sets" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Algorithm" +msgstr "" + +msgctxt "mlClusteringRandomForest|" +msgid "Trees" +msgstr "" + +msgctxt "mlRegressionBoosting|" +msgid "Loss function" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Convergence threshold" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Penalty" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Lasso" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Ridge" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Elastic net parameter (α)" +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "Largest λ within 1 SE of min" +msgstr "" + +msgctxt "Description|" +msgid "Decision Tree" +msgstr "" + +msgctxt "Description|" +msgid "Decision Tree Regression" +msgstr "" + +msgctxt "Description|" +msgid "Support Vector Machine" +msgstr "" + +msgctxt "Description|" +msgid "Support Vector Machine Regression" +msgstr "" + +msgctxt "Description|" +msgid "Decision Tree Classification" +msgstr "" + +msgctxt "Description|" +msgid "Support Vector Machine Classification" +msgstr "" + +msgctxt "Description|" +msgid "Neighborhood-Based" +msgstr "" + +msgctxt "Description|" +msgid "Neighborhood-Based Clustering" +msgstr "" + +msgctxt "mlClassificationDecisionTree|" +msgid "Tables" +msgstr "" + +msgctxt "mlClassificationDecisionTree|" +msgid "Plots" +msgstr "" + +msgctxt "mlClassificationDecisionTree|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClassificationDecisionTree|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClassificationSvm|" +msgid "Tables" +msgstr "" + +msgctxt "mlClassificationSvm|" +msgid "Plots" +msgstr "" + +msgctxt "mlClassificationSvm|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClassificationSvm|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Center type" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Distance" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Euclidean" +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "Manhattan" +msgstr "" + +msgctxt "mlRegressionDecisionTree|" +msgid "Tables" +msgstr "" + +msgctxt "mlRegressionDecisionTree|" +msgid "Plots" +msgstr "" + +msgctxt "mlRegressionDecisionTree|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlRegressionDecisionTree|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlRegressionSvm|" +msgid "Tables" +msgstr "" + +msgctxt "mlRegressionSvm|" +msgid "Plots" +msgstr "" + +msgctxt "mlRegressionSvm|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlRegressionSvm|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlPrediction|" +msgid "Trained model" +msgstr "" + +msgctxt "mlPrediction|" +msgid "e.g., location/model.jaspML" +msgstr "" + +msgctxt "mlPrediction|" +msgid "Features" +msgstr "" + +msgctxt "mlPrediction|" +msgid "Tables" +msgstr "" + +msgctxt "mlPrediction|" +msgid "to" +msgstr "" + +msgctxt "Description|" +msgid "Linear" +msgstr "" + +msgctxt "Description|" +msgid "Linear Regression" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Shrinkage" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"A shrinkage parameter applied to each tree in the expansion. Also known as " +"the learning rate or step-size reduction 0.001 to 0.1 usually work, but a " +"smaller learning rate typically requires more trees." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Interaction depth" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"Integer specifying the maximum depth of each tree (i.e., the highest level " +"of variable interactions allowed. A value of 1 implies an additive model, a " +"value of 2 implies a model with up to 2-way interactions, etc. Default is 1." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Min. observations in node" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"Integer specifying the minimum number of observations in the terminal nodes " +"of the trees. Note that this is the actual number of observations, not the " +"total weight." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Training data used per tree" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"Select the percentage of training data that is used to train each individual " +"tree." +msgstr "" + +msgctxt "Deviance|" +msgid "Deviance" +msgstr "" + +msgctxt "Deviance|" +msgid "Shows the prediction error plotted against the number of trees." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Number of Trees" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Choose how to optimize the model." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Fixed" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Enables you to use a user-specified number of decision trees." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Trees" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "The number of trees." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Optimized" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "" +"Enables you to optimize the prediction error on a validation data set with " +"respect to the number of trees." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Max. trees" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "" +"Sets the maximum number of possible decision trees to be considered. At " +"default, this is set to 100." +msgstr "" + +msgctxt "Oob|" +msgid "Out-of-bag improvement" +msgstr "" + +msgctxt "Oob|" +msgid "" +"Plots the number of trees against the out-of-bag classification accuracy " +"improvement of the model. Accuracy is assessed for the training set." +msgstr "" + +msgctxt "RelativeInfluence|" +msgid "Relative influence" +msgstr "" + +msgctxt "RelativeInfluence|" +msgid "Shows the relative influence of the features." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Min. observations for split" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"The minimum number of observations that must exist in a node in order for a " +"split to be attempted." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Min. observations in terminal" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "The minimum number of observations in any terminal node." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Max. interaction depth" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Set the maximum depth of any node of the final tree." +msgstr "" + +msgctxt "AttemptedSplits|" +msgid "Attempted splits" +msgstr "" + +msgctxt "AttemptedSplits|" +msgid "" +"Shows the splits made by the algorithm, the corresponding features and split " +"points, and the number of observations (which are not missing and are of " +"positive weight) sent left or right by the split. It also shows the " +"improvement in deviance given by the splits." +msgstr "" + +msgctxt "AttemptedSplits|" +msgid "Only show splits in tree" +msgstr "" + +msgctxt "AttemptedSplits|" +msgid "Remove splits that do not occur in the final tree from the table." +msgstr "" + +msgctxt "TreePlot|" +msgid "Decision tree" +msgstr "" + +msgctxt "TreePlot|" +msgid "Creates a plot that visualizes the decision tree and its leafs." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Weights" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Rectangular" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Triangular" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Epanechnikov" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Biweight" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Triweight" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Cosine" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Inverse" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Gaussian" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Rank" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Optimal" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"Sets the weighting scheme for the nearest neighbors. The default rectangular " +"option results in standard knn, while the other options expand the algorithm " +"by weighing the nearest neighbors. See also the kknn package." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Distance" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Euclidian" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Manhattan" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"The distance metric to be used when determining the similarity between " +"nearest neighbors. Can be either Euclidean or Manhattan distance." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Number of Nearest Neighbors" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Enables you to use a user-specified number of nearest neighbors." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Nearest neighbors" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "The number of nearest neighbors to be used." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "" +"Enables you to optimize the prediction error on a validation data set with " +"respect to the number of nearest neighbors." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Max. nearest neighbors" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "" +"Sets the maximum number of possible nearest neighbors to be considered. At " +"default, this is set to 10." +msgstr "" + +msgctxt "OptimPlot|" +msgid "Mean squared error" +msgstr "" + +msgctxt "OptimPlot|" +msgid "Classification accuracy" +msgstr "" + +msgctxt "OptimPlot|" +msgid "" +"For regression, Plots the number of nearest neighbors against the MSE of the " +"model. Accuracy is assessed for the training (and validation) set. For " +"classification, plots the number of nearest neighbors against the " +"classification accuracy of the model. Accuracy is assessed for the training " +"(and validation) set." +msgstr "" + +msgctxt "WeightFunction|" +msgid "Weight function" +msgstr "" + +msgctxt "WeightFunction|" +msgid "Shows how the weights are assigned as a function of the distance." +msgstr "" + +msgctxt "ActivationFunctionPlot|" +msgid "Activation function" +msgstr "" + +msgctxt "ActivationFunctionPlot|" +msgid "Creates a plot of the activation function." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Activation function" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Linear" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Binary" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Logistic sigmoid" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Sine" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Inverse tangent" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Hyperbolic tangent" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "ReLU" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Softplus" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Softsign" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "ELU" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "LReLU" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "SiLU" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Mish" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "GeLU" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"Sets the activation function for the signal in each hidden layer. Available " +"options are:\n" +"- linear: *f(x) = x*\n" +"- Binary: *f(x) = 0 if x < 0, 1 if x > 0\n" +"- Logistic sigmoid: *f(x) = 1 / (1 + e^(-x))*\n" +"- Sine: *f(x) = sin(x)*\n" +"- Cosine: *f(x) = cos(x)*\n" +"- Inverse tangent: *f(x) = arctan(x)*\n" +"- Hyperbolic tangent: *f(x) = tanh(x)*\n" +"- ReLU: *f(x) = 0 if x < 0, x if x > 0*\n" +"- Softplus: *f(x) = log(1 + e^x)*\n" +"- Softsign: *f(x) = x / (abs(x) + 1)*\n" +"- ELU: *f(x) = e^x - 1 if x <= 0, x if x > 0*\n" +"- LReLU: *f(x) = 0.01 * x if x < 0, x if x > 0*\n" +"- SiLU: *f(x) = x / (1 + e^(-x))*\n" +"- Mish: *f(x) = x * tanh(log(1 + e^x))*\n" +"- Gaussian: *f(x) = e * (-x^2)*\n" +"- GeLU: *f(x) = 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))*" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Algorithm" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Backpropagation" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "rprop+" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "rprop-" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "grprop-sag" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "grprop-slr" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"Sets the algorithm for the network training. The backpropagation option is " +"standard for training neural networks, but other options are `rprop+` " +"(default) for resilient backpropagation with backtracing, `rprop-` for " +"resilient backpropagation without backtracing, `gprop-sag` for the globally " +"convergent algorithm that modifies the learning rate associated with the " +"smallest absolute gradient, or `gprop-slr` for the globally convergent " +"algorithm that modifies the learning rate associated with the smallest " +"learning rate itself." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Learning rate" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "The learning rate used by the backpropagation algorithm." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Loss function" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Sum of squares" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Cross-entropy" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "The loss function used." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Stopping criteria loss function" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"The threshold for the partial derivatives of the error function as stopping " +"criteria." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Max. training repetitions" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "The maximum number of repetitions used in training the network." +msgstr "" + +msgctxt "Coefficients|" +msgid "Network weights" +msgstr "" + +msgctxt "Coefficients|" +msgid "" +"Shows the connections in the neural network together with their weights." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Network Topology" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Manual" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Specify the nodes in each hidden layer of the neural network." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Nodes" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Hidden layer " +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Optimize the topology of the network using a genetic algorithm." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Population size" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Size of population used in genetic optimization." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Generations" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Number of generations used in genetic optimization." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Max. number of layers" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Max. nodes in each layer" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Parent selection" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Roulette wheel" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Universal" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Rank" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Tournament" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Random" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "How to select suviving networks." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Candidates" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Number of candidates for tournament selection" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Crossover method" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Uniform" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "One-point" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Multi-point" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "How to crossover two candidate networks." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Mutations" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Reset" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Swap" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Scramble" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Inversion" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "How to mutate a network." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Probability" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "The mutation probability of a random network in each generation." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Survival method" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Fitness-based" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Age-based" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "How to choose which networks survive and die in a generation." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Elitism" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Keep top networks from dying out." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Percentage of top networks to keep." +msgstr "" + +msgctxt "NetworkPlot|" +msgid "Network structure" +msgstr "" + +msgctxt "NetworkPlot|" +msgid "" +"Creates a plot that visualizes the structure (nodes and edges) of the " +"network." +msgstr "" + +msgctxt "OptimPlot|" +msgid "" +"For regression, plots the average mean squared error of the population of " +"neural networks against the number of generations in the evoluationary " +"optimization algorithm. For classification, plots the average classification " +"accuracy of the population of neural networks against the number of " +"generations in the evoluationary optimization algorithm. Accuracy is " +"assessed for the training (and validation) set." +msgstr "" + +msgctxt "AccuracyDecrease|" +msgid "Mean decrease in accuracy" +msgstr "" + +msgctxt "AccuracyDecrease|" +msgid "" +"Displays a figure with the mean decrease in accuracy per feature in the " +"model." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Features per split" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Auto" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Manual" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"Set the number of feature variables that is used within each split in the " +"decision trees. Defaults to auto." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "The number of feature variables in each split." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "The number of trees to be used." +msgstr "" + +msgctxt "NodePurity|" +msgid "Total increase in node purity" +msgstr "" + +msgctxt "NodePurity|" +msgid "" +"Displays a figure with total increase in node purity per feature in the " +"model." +msgstr "" + +msgctxt "Oob|" +msgid "Out-of-bag error" +msgstr "" + +msgctxt "Oob|" +msgid "Out-of-bag accuracy" +msgstr "" + +msgctxt "Oob|" +msgid "" +"Plots the number of trees against the out-of-bag mean squared error " +"(regression) or accuracy (classification) of the model." +msgstr "" + +msgctxt "CoefficientTable|" +msgid "Coefficients" +msgstr "" + +msgctxt "CoefficientTable|" +msgid "Shows a table containing the regression coefficients." +msgstr "" + +msgctxt "CoefficientTable|" +msgid "Confidence interval" +msgstr "" + +msgctxt "CoefficientTable|" +msgid "Display confidence intervals around estimated regression coefficients." +msgstr "" + +msgctxt "CoefficientTable|" +msgid "The confidence level for the interval." +msgstr "" + +msgctxt "CoefficientTable|" +msgid "Display equation" +msgstr "" + +msgctxt "CoefficientTable|" +msgid "" +"Display the regression equation with the estimated values of the " +"coefficients." +msgstr "" + +msgctxt "Intercept|" +msgid "Include intercept" +msgstr "" + +msgctxt "Intercept|" +msgid "Whether to include an intercept in the regression formula." +msgstr "" + +msgctxt "VariablesFormRegularizedRegression|" +msgid "Target" +msgstr "" + +msgctxt "VariablesFormRegularizedRegression|" +msgid "In this box, the variable that needs to be predicted should be entered." +msgstr "" + +msgctxt "VariablesFormRegularizedRegression|" +msgid "Features" +msgstr "" + +msgctxt "VariablesFormRegularizedRegression|" +msgid "" +"In this box, the variables that provide information about the target " +"variable should be entered." +msgstr "" + +msgctxt "VariablesFormRegularizedRegression|" +msgid "Weights" +msgstr "" + +msgctxt "VariablesFormRegularizedRegression|" +msgid "" +"In this box, an optional variable containing case weights can be entered." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Radial" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Polynomial" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Sigmoid" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "" +"The kernel used in training and predicting. Possible kernels are 'linear', " +"'radial', 'polynomial', and 'sigmoid'." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Degree" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "The degree of polynomial used." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Gamma parameter" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "The gamma parameter used." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "r parameter" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "The complexity parameter used." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Tolerance of termination criterion" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "The tolerance of termination criterion." +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "Epsilon" +msgstr "" + +msgctxt "AlgorithmicSettings|" +msgid "The epsilon parameter in the insensitive-loss function." +msgstr "" + +msgctxt "SupportVectors|" +msgid "Support vectors" +msgstr "" + +msgctxt "SupportVectors|" +msgid "" +"Shows a table containing the data (points) indicated as support vectors by " +"the algorithm." +msgstr "" + +msgctxt "AndrewsCurve|" +msgid "Andrews curves" +msgstr "" + +msgctxt "AndrewsCurve|" +msgid "" +"Is a way to visualize structure in high-dimensional data. Lines that cluster " +"are observations that are more alike." +msgstr "" + +msgctxt "ClusterDensity|" +msgid "Cluster densities" +msgstr "" + +msgctxt "ClusterDensity|" +msgid "" +"For each feature variable, generates a plot showing the overlapping " +"densities for the clusters." +msgstr "" + +msgctxt "ClusterDensity|" +msgid "Group into one figure" +msgstr "" + +msgctxt "ClusterDensity|" +msgid "Group the density plots per feature into a single figure." +msgstr "" + +msgctxt "ClusterMatrix|" +msgid "Cluster matrix plot" +msgstr "" + +msgctxt "ClusterMatrix|" +msgid "" +"Creates a *n* x *n* plot that visualizes to which cluster every observation " +"belongs according to the current model." +msgstr "" + +msgctxt "ClusterMeans|" +msgid "Cluster means" +msgstr "" + +msgctxt "ClusterMeans|" +msgid "" +"Creates a plot that visualizes and compares the mean of the feature " +"variables in each cluster." +msgstr "" + +msgctxt "ClusterMeans|" +msgid "Display barplot" +msgstr "" + +msgctxt "ClusterMeans|" +msgid "Transform the cluster mean figure into a barplot." +msgstr "" + +msgctxt "ClusterMeans|" +msgid "Group into one figure" +msgstr "" + +msgctxt "ClusterMeans|" +msgid "Group the plots per feature into a single figure." +msgstr "" + +msgctxt "DataSplit|" +msgid "Data split" +msgstr "" + +msgctxt "DataSplit|" +msgid "" +"Shows how the data is split into training (and validation), and test set." +msgstr "" + +msgctxt "DecisionBoundary|" +msgid "Decision boundary matrix" +msgstr "" + +msgctxt "DecisionBoundary|" +msgid "" +"Creates a *n* x *n* plot that visualizes how every observation would be " +"classified if predicted through the current model. Boundaries between " +"classes are visualized. Can only be made for numeric features." +msgstr "" + +msgctxt "DecisionBoundary|" +msgid "Legend" +msgstr "" + +msgctxt "DecisionBoundary|" +msgid "Show a legend next to the figure." +msgstr "" + +msgctxt "DecisionBoundary|" +msgid "Add data points" +msgstr "" + +msgctxt "DecisionBoundary|" +msgid "Show the observations in the data set as points in the plot." +msgstr "" + +msgctxt "ElbowMethod|" +msgid "Elbow method" +msgstr "" + +msgctxt "ElbowMethod|" +msgid "" +"Generates a plot with the total within sum of squares on the y-axis and the " +"number of clusters on the x-axis. This plot can be used for determining the " +"optimal number of clusters. The plot shows three curves using AIC, BIC, and " +"'elbow method' optimization." +msgstr "" + +msgctxt "PredictivePerformance|" +msgid "Predictive performance" +msgstr "" + +msgctxt "PredictivePerformance|" +msgid "" +"Plots the true values of the observations in the test set against their " +"predicted values." +msgstr "" + +msgctxt "RocCurve|" +msgid "ROC curves" +msgstr "" + +msgctxt "RocCurve|" +msgid "Displays ROC curves for each class predicted against all other classes." +msgstr "" + +msgctxt "Tsne|" +msgid "t-SNE cluster plot" +msgstr "" + +msgctxt "Tsne|" +msgid "" +"Generates a t-SNE plot of the clustering output. t-SNE plots are used for " +"visualizing high-dimensional data in a low-dimensional space of two " +"dimensions aiming to illustrate the relative distances between data " +"observations. The t-SNE two-dimensional space makes the axes " +"uninterpretable. A t-SNE plot seeks to give an impression of the relative " +"distances between observations and clusters. To recreate the same t-SNE plot " +"across several clustering analyses you can set their seed to the same value, " +"as the t-SNE algorithm uses random starting values." +msgstr "" + +msgctxt "Tsne|" +msgid "Legend" +msgstr "" + +msgctxt "Tsne|" +msgid "Show a legend next to the figure." +msgstr "" + +msgctxt "Tsne|" +msgid "Add data labels" +msgstr "" + +msgctxt "Tsne|" +msgid "" +"Add the row numbers of the observations in the data set as labels to the " +"plot." +msgstr "" + +msgctxt "ClassProportions|" +msgid "Class proportions" +msgstr "" + +msgctxt "ClassProportions|" +msgid "" +"Displays a table that shows the proportions of each class in the data set, " +"training (and validaton), and test set." +msgstr "" + +msgctxt "ClusterInfo|" +msgid "Cluster information" +msgstr "" + +msgctxt "ClusterInfo|" +msgid "" +"Displays the size of each cluster and the explained proportion of within-" +"cluster heterogeneity. The latter is the cluster within sum of squares " +"divided by its total over the various clusters. These outputs are shown by " +"default." +msgstr "" + +msgctxt "ClusterInfo|" +msgid "Within sum of squares" +msgstr "" + +msgctxt "ClusterInfo|" +msgid "" +"Adds a row with the within sum of squares of each cluster to the table. This " +"option is selected by default." +msgstr "" + +msgctxt "ClusterInfo|" +msgid "Silhouette score" +msgstr "" + +msgctxt "ClusterInfo|" +msgid "Adds a row with the silhouette score of each cluster to the table." +msgstr "" + +msgctxt "ClusterInfo|" +msgid "Centers" +msgstr "" + +msgctxt "ClusterInfo|" +msgid "" +"Adds a row with the center per feature of each cluster to the table. The " +"center can be the mean, median or mode depending on the clustering algorithm." +msgstr "" + +msgctxt "ClusterInfo|" +msgid "Between sum of squares" +msgstr "" + +msgctxt "ClusterInfo|" +msgid "" +"Adds a note with the between sum of squares of the cluster model to the " +"table." +msgstr "" + +msgctxt "ClusterInfo|" +msgid "Total sum of squares" +msgstr "" + +msgctxt "ClusterInfo|" +msgid "" +"Adds a note with the total sum of squares of the cluster model to the table." +msgstr "" + +msgctxt "ClusterMeans|" +msgid "Shows a table containing the cluster means for each feature variable." +msgstr "" + +msgctxt "ConfusionMatrix|" +msgid "Confusion matrix" +msgstr "" + +msgctxt "ConfusionMatrix|" +msgid "" +"Displays a table that shows the observed classes against the predicted " +"classes. Used to assess model accuracy." +msgstr "" + +msgctxt "ConfusionMatrix|" +msgid "Display proportions" +msgstr "" + +msgctxt "ConfusionMatrix|" +msgid "Displays proportions in the confusion matrix instead of counts." +msgstr "" + +msgctxt "ExplainPredictions|" +msgid "Explain predictions" +msgstr "" + +msgctxt "ExplainPredictions|" +msgid "Cases" +msgstr "" + +msgctxt "ExplainPredictions|" +msgid "The test set index of the first row to be displayed in the table." +msgstr "" + +msgctxt "ExplainPredictions|" +msgid "to" +msgstr "" + +msgctxt "ExplainPredictions|" +msgid "The test set index of the last row to be displayed in the table." +msgstr "" + +msgctxt "FeatureImportance|" +msgid "Feature importance" +msgstr "" + +msgctxt "FeatureImportance|" +msgid "Shows the available feature importance metrics for the fitted model." +msgstr "" + +msgctxt "ModelPerformance|" +msgid "Model performance" +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "Cluster Determination" +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "Choose how to determine the number of clusters in the model." +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "Fixed" +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "" +"Enables you to generate a fixed amount of clusters. This allows you to " +"generate your own specified number of clusters, and thus, optimize manually." +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "Clusters" +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "The number of clusters to be fitted." +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "Optimized according to" +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "" +"Enables you to choose an optimization method. BIC optimization is set as " +"default." +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "" +"The method of optimization. The options are AIC, BIC, and silhouette. The " +"AIC uses the within sum of squares (within-cluster variation), the number of " +"generated clusters and the number of dimensions for optimizing the " +"clustering output. The BIC uses the within sum of squares (within-cluster " +"variation), the number of generated clusters, the number of dimensions, and " +"the sample size for optimizing the clustering output. The silhouette value " +"uses the similarity of observations within a cluster and their dissimilarity " +"to other clusters for optimizing the clustering output." +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "Max. clusters" +msgstr "" + +msgctxt "ClusterDetermination|" +msgid "" +"Sets the maximum number of possible clusters to be generated. At default, " +"this is set to 10." +msgstr "" + +msgctxt "DataSplit|" +msgid "Choose how to create the test set." +msgstr "" + +msgctxt "DataSplit|" +msgid "" +"Choose a percentage to randomly sample from your data to derive prediction " +"error. Generates an internal indicator variable that indicates whether the " +"observation is included (1) or excluded (0) from the test set." +msgstr "" + +msgctxt "DataSplit|" +msgid "The percentage of observations to use for the test set." +msgstr "" + +msgctxt "DataSplit|" +msgid "" +"Add the generated test set indicator from the option above to your data set." +msgstr "" + +msgctxt "DataSplit|" +msgid "The column name for the generated test set indicator." +msgstr "" + +msgctxt "DataSplit|" +msgid "The variable in the data set that is used as the test set indicator." +msgstr "" + +msgctxt "DataSplit|" +msgid "Choose how to create the validation set." +msgstr "" + +msgctxt "DataSplit|" +msgid "" +"Randomly sample a percentage from the remaining training data (after " +"selecting the test set)." +msgstr "" + +msgctxt "DataSplit|" +msgid "The percentage of observations to use for the validation set." +msgstr "" + +msgctxt "DataSplit|" +msgid "Partition the remaining data in *k* parts." +msgstr "" + +msgctxt "DataSplit|" +msgid "The number of folds to be used." +msgstr "" + +msgctxt "DataSplit|" +msgid "Partition the remaining data in *n* parts." +msgstr "" + +msgctxt "ExportResults|" +msgid "" +"Generates a new column in your dataset with the values of your regression " +"result. This gives you the option to inspect, cluster, or predict the " +"generated values." +msgstr "" + +msgctxt "ExportResults|" +msgid "The column name for the predicted values." +msgstr "" + +msgctxt "ExportResults|" +msgid "The file path for the saved model." +msgstr "" + +msgctxt "ExportResults|" +msgid "When clicked, the model is exported to the specified file path." +msgstr "" + +msgctxt "ScaleVariables|" +msgid "Scale features" +msgstr "" + +msgctxt "ScaleVariables|" +msgid "" +"Standardizes the continuous features in the dataset. Standardization ensures " +"that values of features from different scales range into a specific similar " +"scale. As a result, standardizing provides numerical stability. JASP uses " +"the Z-score standardization of a mean of 0 and a standard deviation of 1. " +"This option is selected by default." +msgstr "" + +msgctxt "SetSeed|" +msgid "Set seed" +msgstr "" + +msgctxt "SetSeed|" +msgid "" +"Gives the option to set a seed for your analysis. Setting a seed will " +"exclude random processes influencing an analysis. For example, setting a " +"seed makes it possible to re-run analyses with the same data splits." +msgstr "" + +msgctxt "SetSeed|" +msgid "The value of the seed." +msgstr "" + +msgctxt "VariablesFormClassification|" +msgid "Target" +msgstr "" + +msgctxt "VariablesFormClassification|" +msgid "In this box, the variable that needs to be predicted should be entered." +msgstr "" + +msgctxt "VariablesFormClassification|" +msgid "Features" +msgstr "" + +msgctxt "VariablesFormClassification|" +msgid "" +"In this box, the variables that provide information about the target " +"variable should be entered." +msgstr "" + +msgctxt "VariablesFormClustering|" +msgid "Features" +msgstr "" + +msgctxt "VariablesFormClustering|" +msgid "" +"In this box, the variables are need to be considered by the clustering " +"algorithm should be entered." +msgstr "" + +msgctxt "VariablesFormRegression|" +msgid "Target" +msgstr "" + +msgctxt "VariablesFormRegression|" +msgid "In this box, the variable that needs to be predicted should be entered." +msgstr "" + +msgctxt "VariablesFormRegression|" +msgid "Features" +msgstr "" + +msgctxt "VariablesFormRegression|" +msgid "" +"In this box, the variables that provide information about the target " +"variable should be entered." +msgstr "" + +msgctxt "mlClassificationBoosting|" +msgid "" +"Boosting works by sequentially adding features to an decision tree ensemble, " +"each one correcting its predecessor. However, instead of changing the " +"weights for every incorrect classified observation at every iteration, " +"Boosting method tries to fit the new feature to the residual errors made by " +"the previous feature.\n" +"### Assumptions\n" +"- The target variable is a nominal or ordinal variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variables." +msgstr "" + +msgctxt "mlClassificationDecisionTree|" +msgid "" +"Decision Trees is a supervised learning algorithm that uses a decision tree " +"as a predictive model to go from observations about an item (represented in " +"the roots of the tree) to conclusions about the item's target value " +"(represented in the endpoints of the tree).\n" +"### Assumptions\n" +"- The target is a nominal or ordinal variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variables." +msgstr "" + +msgctxt "mlClassificationKnn|" +msgid "" +"K-nearest neighbors is a method of classification that looks at the *k* " +"number of feature observations that are most similar to new observations to " +"make a prediction for their class assignments. The number of nearest " +"neighbors is intrinsincly linked to model complexity, as small numbers " +"increase the flexibility of the model.\n" +"### Assumptions\n" +"- The target is a nominal or ordinal variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variables." +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "" +"Linear Discriminant Analysis (LDA) is a method of classification that aims " +"to find *p - 1* components that discriminate best between the classes in the " +"target variable. LDA is a linear classifier, meaning that the decision " +"boundaries between classes are linear.\n" +"### Assumptions\n" +"- The target variable is a nominal or ordinal variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal " +"variables.\n" +"- Equality of class means: The class means should be equal, can be checked " +"with the corresponding table.\n" +"- Equality of covariance matrices: The covariance matrices should be equal, " +"can be checked with the corresponding table.\n" +"- Multicollinearity: The classes should not correlate within each other, can " +"be checked with the corresponding table." +msgstr "" + +msgctxt "mlClassificationLda|" +msgid "Multivariate normality" +msgstr "" + +msgctxt "mlClassificationNeuralNetwork|" +msgid "" +"Feedforward neural networks are predictive algorithms inspired by the " +"biological neural networks that constitute brains. A neuron (node) that " +"receives a signal then processes it and can send signals to neurons " +"connected to it. The signal at a node is a real number, and the output of " +"each node is computed by sending the signal trough the activation function. " +"The number of layers and nodes in the network is intrinsincly linked to " +"model complexity, as high numbers increase the flexibility of the model.\n" +"### Assumptions\n" +"- The target is a nominal or ordinal variable.\n" +"- The feature variables consist of continuous variables." +msgstr "" + +msgctxt "mlClassificationRandomForest|" +msgid "" +"Random Forest is a method of classification that creates a set of decision " +"trees that consists of a large number of individual trees which operate as " +"an ensemble. Each individual tree in the random forest returns a class " +"prediction and the class with the most votes becomes the model's " +"prediction.\n" +"### Assumptions\n" +"- The target variable is a nominal or ordinal variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variables." +msgstr "" + +msgctxt "mlClassificationSvm|" +msgid "" +"Support Vector Machines is a supervised learning algorithm that maps " +"training examples to points in space so as to maximise the width of the gap " +"between the two categories. New examples are then mapped into that same " +"space and predicted to belong to a category based on which side of the gap " +"they fall.\n" +"### Assumptions\n" +"- The target is a nominal or ordinal variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variables." +msgstr "" + +msgctxt "mlClusteringDensityBased|" +msgid "" +"Density-based clustering is a soft clustering method where clusters are " +"constructed as maximal sets of points that are connected to points whose " +"density exceeds some threshold. The density is produced by the concept that " +"for each point within a cluster, the neighborhood within a given radius has " +"to contain at least a minimum amount of points, that results in the density " +"of that neighborhood to exceed a certain threshold. A density-based cluster " +"is recognized by points having a higher density than points outside of the " +"cluster. The set of all high-density points is called the density level. The " +"points that do not exceed a density level are identified as outliers. The " +"density level influences the amount of generated clusters.\n" +"### Assumptions\n" +"- The data consists of continuous variables.\n" +"- (Normally distributed data aids the clustering process)." +msgstr "" + +msgctxt "mlClusteringFuzzyCMeans|" +msgid "" +"Fuzzy c-means clustering is a soft partitioning method that provides an " +"output that contains the degree of association for each observation to each " +"cluster. This makes it possible for data observations to be partially " +"assigned to multiple clusters and give a degree of confidence about cluster " +"membership. Fuzzy c-means' approach is quite similar to that of k-means " +"clustering, apart from its soft approach.\n" +"### Assumptions\n" +"- The data consists of continuous variables.\n" +"- (Normally distributed data aids the clustering process)." +msgstr "" + +msgctxt "mlClusteringHierarchical|" +msgid "" +"Hierarchical clustering is a hard partitioning algorithm which aims to " +"partition data into several clusters, where each observation belongs to only " +"one group. The data is divided in such a way that the degree of similarity " +"between two data observations is maximal if they belong to the same group " +"and minimal if not.\n" +"### Assumptions\n" +"- The data consists of continuous variables.\n" +"- (Normally distributed data aids the clustering process)." +msgstr "" + +msgctxt "mlClusteringKMeans|" +msgid "" +"Neighborhood-Based clustering methods are a set of hard partitioning " +"algorithm which aims to partition data into several clusters, where each " +"observation belongs to only one group. The data is divided in such a way " +"that the degree of similarity between two data observations is maximal if " +"they belong to the same group and minimal if not.\n" +"### Assumptions\n" +"- The data consists of continuous variables.\n" +"- (Normally distributed data aids the clustering process)." +msgstr "" + +msgctxt "mlClusteringRandomForest|" +msgid "" +"Random Forest clustering is a hard partitioning algorithm which aims to " +"partition data into several clusters, where each observation belongs to only " +"one group. This clustering method uses the Random Forest algorithm in an " +"unsupervised way, with the outcome variable 'y' set to NULL. The Random " +"Forest algorithm generates a proximity matrix which gives an estimate of the " +"distance between observations based on the frequency of observations ending " +"up in the same leaf node.\n" +"### Assumptions\n" +"- The data consists of continuous variables.\n" +"- (Normally distributed data aids the clustering process)." +msgstr "" + +msgctxt "mlPrediction|" +msgid "Cases" +msgstr "" + +msgctxt "mlPrediction|" +msgid "Add features" +msgstr "" + +msgctxt "mlPrediction|" +msgid "Explain predictions" +msgstr "" + +msgctxt "mlRegressionBoosting|" +msgid "" +"Boosting works by sequentially adding features to an decision tree ensemble, " +"each one correcting its predecessor. Boosting tries to fit the new feature " +"to the residual errors made by the previous feature.\n" +"### Assumptions\n" +"- The target variable is a continuous variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variables." +msgstr "" + +msgctxt "mlRegressionBoosting|" +msgid "The loss function used." +msgstr "" + +msgctxt "mlRegressionDecisionTree|" +msgid "" +"Decision Trees is a supervised learning algorithm that uses a decision tree " +"as a predictive model to go from observations about an item (represented in " +"the roots of the tree) to conclusions about the item's target value " +"(represented in the endpoints of the tree).\n" +"### Assumptions\n" +"- The target variable is a continuous variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variables." +msgstr "" + +msgctxt "mlRegressionKnn|" +msgid "" +"K-nearest neighbors is a method of regression that looks at the *k* number " +"of feature observations that are most similar to new observations to make a " +"prediction for their values. The number of nearest neighbors is intrinsincly " +"linked to model complexity, as small numbers increase the flexibility of the " +"model.\n" +"### Assumptions\n" +"- The target variable is a continuous variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variables." +msgstr "" + +msgctxt "mlRegressionLinear|" +msgid "" +"Linear regression allows the user to model a linear relationship between one " +"or more features (predictors) and a continuous dependent (target) variable." +msgstr "" + +msgctxt "mlRegressionLinear|" +msgid "Tables" +msgstr "" + +msgctxt "mlRegressionLinear|" +msgid "Plots" +msgstr "" + +msgctxt "mlRegressionLinear|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlRegressionLinear|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlRegressionNeuralNetwork|" +msgid "" +"Feedforward neural networks are predictive algorithms inspired by the " +"biological neural networks that constitute brains. A neuron (node) that " +"receives a signal then processes it and can send signals to neurons " +"connected to it. The signal at a node is a real number, and the output of " +"each node is computed by sending the signal trough the activation function. " +"The number of layers and nodes in the network is intrinsincly linked to " +"model complexity, as high numbers increase the flexibility of the model.\n" +"### Assumptions\n" +"- The target variable is a continuous variable.\n" +"- The feature variables consist of continuous." +msgstr "" + +msgctxt "mlRegressionRandomForest|" +msgid "" +"Random Forest is a method of regression that creates a set of decision trees " +"that consists of a large number of individual trees which operate as an " +"ensemble.\n" +"### Assumptions\n" +"- The target variable is a continuous variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variables." +msgstr "" + +msgctxt "mlRegressionRegularized|" +msgid "" +"Regularized linear regression is an adaptation of linear regression in which " +"the coefficients are shrunken towards 0. This is done by applying a penalty " +"(e.g., ridge, lasso, or elastic net). The parameter λ controls the degree to " +"which parameters are shrunken.\n" +"### Assumptions\n" +"- The target variable is a continuous variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variables." +msgstr "" + +msgctxt "mlRegressionSvm|" +msgid "" +"Support Vector Machines is a supervised learning algorithm that maps " +"training examples to points in space so as to maximise the width of the gap " +"between the two categories. New examples are then mapped into that same " +"space and predicted to belong to a category based on which side of the gap " +"they fall.\n" +"### Assumptions\n" +"- The target variable is a continuous variable.\n" +"- The feature variables consist of continuous, nominal, or ordinal variable" +msgstr "" + +msgctxt "Description|" +msgid "Naive Bayes" +msgstr "" + +msgctxt "Description|" +msgid "Naive Bayes Classification" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Tree Complexity" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Enables you to use a user-specified complexity penalty." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Complexity penalty" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "" +"The complexity penalty to be used. Any split that does not decrease the " +"overall lack of fit by a factor of this parameter is not attempted." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "" +"Enables you to optimize the prediction error on a validation data set with " +"respect to the complexity penalty." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Max. complexity penalty" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "" +"Sets the maximum value of the complexity penalty to be considered. At " +"default, this is set to 1." +msgstr "" + +msgctxt "OptimPlot|" +msgid "" +"For regression, Plots the complexity penalty against the MSE of the model. " +"Accuracy is assessed for the training (and validation) set. For " +"classification, plots the complexity penalty against the classification " +"accuracy of the model. Accuracy is assessed for the training (and " +"validation) set." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Costs of Contraints Violation" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Enables you to use a user-specified cost of constraints violation." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Violation cost" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "" +"Enables you to optimize the prediction error on a validation data set with " +"respect to the cost of constraints violation." +msgstr "" + +msgctxt "ModelOptimization|" +msgid "Max. violation cost" +msgstr "" + +msgctxt "ModelOptimization|" +msgid "" +"Sets the maximum value of the cost of constraints violation to be " +"considered. At default, this is set to 5." +msgstr "" + +msgctxt "OptimPlot|" +msgid "" +"For regression, Plots the cost of contraints violation against the MSE of " +"the model. Accuracy is assessed for the training (and validation) set. For " +"classification, plots the cost of contraints violation against the " +"classification accuracy of the model. Accuracy is assessed for the training " +"(and validation) set." +msgstr "" + +msgctxt "mlClassificationNaiveBayes|" +msgid "" +"Naive Bayes computes the conditional posterior probabilities of a " +"categorical class variable given independent predictor variables using the " +"Bayes rule.\n" +"### Assumptions\n" +"- The target variable is a nominal or ordinal variable.\n" +"- The features are independent.\n" +"- The features are normally distributed given the target class." +msgstr "" + +msgctxt "mlClassificationNaiveBayes|" +msgid "Tables" +msgstr "" + +msgctxt "mlClassificationNaiveBayes|" +msgid "Posterior statistics" +msgstr "" + +msgctxt "mlClassificationNaiveBayes|" +msgid "" +"Show tables with the posterior statistics. For numeric features, the table " +"contains the mean and standard deviation of the feature given the target " +"class. For categorical features, the table displays the conditional " +"probabilities given the target class." +msgstr "" + +msgctxt "mlClassificationNaiveBayes|" +msgid "Plots" +msgstr "" + +msgctxt "mlClassificationNaiveBayes|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClassificationNaiveBayes|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClassificationNaiveBayes|" +msgid "Smoothing parameter" +msgstr "" + +msgctxt "mlClassificationNaiveBayes|" +msgid "" +"A positive double controlling the amount of Laplace smoothing applied. The " +"default (0) disables Laplace smoothing alltogether." +msgstr "" + +msgctxt "FeatureImportance|" +msgid "Permutations" +msgstr "" + +msgctxt "FeatureImportance|" +msgid "" +"Sets the number of permutations on which the mean dropout loss is based." +msgstr "" + +msgctxt "Description|" +msgid "Model-Based" +msgstr "" + +msgctxt "Description|" +msgid "Model-Based Clustering" +msgstr "" + +msgctxt "ClusterMatrix|" +msgid "Display components" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "" +"Model-based clustering is based on parameterized finite Gaussian mixture " +"models. The models are estimated by EM algorithm initialized by hierarchical " +"model-based agglomerative clustering." +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "Tables" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "Parameter estimates" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "" +"Shows tables containing the model parameters for each cluster and feature " +"variable." +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "Plots" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "Model" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "Auto" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "EII" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "VII" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "EEI" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "VEI" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "EVI" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "EEE" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "VEE" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "EVE" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "VVE" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "EEV" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "VEV" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "EVV" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "VVV" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "Choose the model to be fitted in the EM step of the clustering." +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "Max. iterations" +msgstr "" + +msgctxt "mlClusteringModelBased|" +msgid "The maximum number of iterations for the M-step in the algorithm." +msgstr "" + +msgctxt "mlPrediction|" +msgid "Predictions for new data" +msgstr "" + +msgctxt "ConfusionMatrix|" +msgid "Transpose matrix" +msgstr "" + +msgctxt "ConfusionMatrix|" +msgid "Transposes the confusion matrix." +msgstr "" + +msgctxt "ExplainPredictions|" +msgid "" +"Shows a decomposition of the predictions of the model into contributions " +"that can be attributed to individual model features. This feature uses the " +"breakdown algoritm from the `ibreakdown` R package. For more details about " +"this method, see Gosiewska and Biecek (2019)." +msgstr "" + +msgctxt "DataSplit|" +msgid "e.g., testSet" +msgstr "" + +msgctxt "Description|" +msgid "Logistic / Multinomial" +msgstr "" + +msgctxt "Description|" +msgid "Logistic / Multinomial Regression Classification" +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "" +"Logistic regression is a statistical method used to model the relationship " +"between a binary target variable (with two possible outcomes) and one or " +"more feature variables. It predicts the probability of a specific outcome by " +"using a logistic function, which ensures that the predicted probabilities " +"are between 0 and 1. Multinomial regression extends logistic regression to " +"handle target variables with more than two categories. Instead of predicting " +"binary outcomes, multinomial regression is used for scenarios where the " +"target variable has three or more unordered categories." +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "Tables" +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "Plots" +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "Training Parameters" +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "Algorithmic Settings" +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "Link function (for binary classification)" +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "Logit" +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "Probit" +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "Cauchit" +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "Complimentary log-log" +msgstr "" + +msgctxt "mlClassificationLogisticMultinomial|" +msgid "Log" +msgstr "" + +msgctxt "mlPrediction|" +msgid "" +"The prediction analysis enables you to load a trained machine learning model " +"and apply it to new data. It is important that the features in the new " +"dataset have the same names as in the original dataset used for training." +msgstr "" + +msgctxt "DataSplit|" +msgid "" +"Use an indicator variable to select data for the test set. This indicator " +"should be a column of type scale in your data that consists of only 0 " +"(excluded from the test set) and 1 (included in the test set). The data will " +"then be split into a training (and validation if requested) set (0), and a " +"test set (1) according to your indicator." +msgstr "" + +msgctxt "ExportResults|" +msgid "Add probabilities (classification only)" +msgstr "" + +msgctxt "ExportResults|" +msgid "" +"In classification analyses, append the predicted probabilities for each " +"class to the data. For neural networks, this option provides the output of " +"the final layer." +msgstr "" + +msgctxt "Description|" +msgid "Supervised Learning" +msgstr "" + +msgctxt "Description|" +msgid "Unsupervised Learning" +msgstr "" + +msgctxt "ModelPerformance|" +msgid "" +"Displays available model performance metrics. For regression, these metrics " +"include mean squared error (MSE), root mean squared error (RMSE), R2 and more. For classification, these metrics include precision, recall, " +"the F1-score, support, AUC (area under the ROC curve) and more. For " +"clustering, these metrics include entropy, Dunn index and more." +msgstr ""