MLfromScratch

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.

Package Description

1. linear_model
   • LinearRegression
   • SGDRegressor
   • SGDClassifier
2. tree
   • DecisionTreeClassifier
   • DecisionTreeRegressor
3. neighbors
   • KNeighborsRegressor
   • KNeighborsClassifier
4. naive_bayes
   • GaussianNB
   • MultinomialNB
5. cluster
   • KMeans
   • AgglomerativeClustering
   • DBSCAN
   • MeanShift
6. ensemble
   • RandomForestClassifier
   • RandomForestRegressor
   • GradientBoostingRegressor
   • GradientBoostingClassifier
   • VotingClassifier
7. 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
8. model_selection
   • train_test_split
   • KFold
9. preprocessing
   • StandardScaler
   • MinMaxScaler
   • RobustScaler
   • LabelEncoder
   • OneHotEncoder

Contributors

• Aditya Jain