Library for machine learning where all algorithms are implemented from scratch. Used only numpy.
Here I tried to keep package modules in same structure as sklearn.
- linear_model
- LinearRegression
- SGDRegressor
- SGDClassifier
- tree
- DecisionTreeClassifier
- DecisionTreeRegressor
- neighbors
- KNeighborsRegressor
- KNeighborsClassifier
- naive_bayes
- GaussianNB
- MultinomialNB
- cluster
- KMeans
- AgglomerativeClustering
- DBSCAN
- MeanShift
- ensemble
- RandomForestClassifier
- RandomForestRegressor
- GradientBoostingRegressor
- GradientBoostingClassifier
- VotingClassifier
- metrics
- mean_squared_error
- root_mean_squared_error
- mean_absolute_error
- r2_score
- accuracy_score
- confusion_matrix
- roc_auc_score
- roc_curve
- precision_score
- recall_score
- sensitivity_score
- specificity_score
- f1_score
- adjusted_rand_score
- model_selection
- train_test_split
- KFold
- preprocessing
- StandardScaler
- MinMaxScaler
- RobustScaler
- LabelEncoder
- OneHotEncoder