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Update machine-learning-evaluate-model-performance.md #640

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Expand Up @@ -33,7 +33,7 @@ Azure Machine Learning supports model evaluation through two of its main machine
## Evaluation vs. Cross Validation
Evaluation and cross validation are standard ways to measure the performance of your model. They both generate evaluation metrics that you can inspect or compare against those of other models.

[Evaluate Model][evaluate-model] expects a scored dataset as input (or 2 in case you would like to compare the performance of 2 different models). This means that you need to train your model using the [Train Model][train-model] module and make predictions on some dataset using the [Score Model][score-model] module, before you can evaluate the results. The evaluation is the based on the scored labels/probabilities along with the true labels, all of which are output by the [Score Model][score-model] module.
[Evaluate Model][evaluate-model] expects a scored dataset as input (or 2 in case you would like to compare the performance of 2 different models). This means that you need to train your model using the [Train Model][train-model] module and make predictions on some dataset using the [Score Model][score-model] module, before you can evaluate the results. The evaluation is based on the scored labels/probabilities along with the true labels, all of which are output by the [Score Model][score-model] module.

Alternatively, you can use cross validation to perform a number of train-score-evaluate operations (10 folds) automatically on different subsets of the input data. The input data is split into 10 parts, where one is reserved for testing, and the other 9 for training. This process is repeated 10 times and the evaluation metrics are averaged. This helps in determining how well a model would generalize to new datasets. The [Cross-Validate Model][cross-validate-model] module takes in an untrained model and some labeled dataset and outputs the evaluation results of each of the 10 folds, in addition to the averaged results.

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