AlgoMaster 0.0.18

The Classifier class is a versatile implementation of various machine learning classifiers, including logistic regression, k-nearest neighbors, naive Bayes, random forests, and support vector machines, among others. It provides methods for training, evaluating, and using these classifiers, as well as ensemble methods and hyperparameter tuning.

Project Title

Machine Learning Models Made Simple

Guide

Classfication model

Classifier->(X,Y,test_size=0.2,random_state=20,scaler=None)[class]

            [function]

            model_training

            model_accuracy(y_test_f,y_pred_f,model_name,model_obj)



            logistic_regression

            kneighbors_classifier

            gaussian_nb

            bagging_classifier

            extra_trees_classifier

            ridge_classifier

            sgd_classifier

            random_forest_classifier

            xgb_classifier

            ada_boost_classifier

            bernoulli_nb

            gradient_boosting_classifier

            decision_tree_classifier

            svc



            [Hyperparameter tuning]



            hyperparameter_tuning



            logistic_hyperparameter

            knn_hyperparameter

            gaussian_nb_hyperparameter

            bernoulli_nb_hyperparameter

            ridge_hyperparameter

            adaboost_hyperparameter

            gradient_boosting_hyperparameter

            svc_hyperparameter

            decision_tree_hyperparameter

Regression model

Regressor->(X,Y,test_size=0.2,random_state=20,scaler=None)[class]

            [function]

            model_training

            model_accuracy(y_test_f,y_pred_f,model_name,model_obj)



            linear_regression

            ridge_regression

            lasso_regression

            elastic_net_regression

            sgd_regression

            random_forest_regression

            kneighbors_regression

            decision_tree_regression

            ada_boost_regression

            xgboost_regression

            gradient_boosting_regression

            theilsen_regression

            ransac_regression

            lasso_lars_regression

            lars_regression

            orthogonal_regression

            huber_regression

            svr

            passive_aggressive_regression

            ard_regression

            bayesian_ridge_regression

            bagging_regression

            extra_trees_regression