Metadata-Version: 2.1
Name: AlgoMaster
Version: 0.0.18
Summary: 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.
Author: sajo sam
Author-email: <sajosamambalakara@gmail.com>
Keywords: machine learning,classifiers,logistic regression,k-nearest neighbors,naive Bayes,random forests,support vector machines,ensemble methods,hyperparameter tuning
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: sklearn
Requires-Dist: xgboost
Requires-Dist: scikit-learn


# 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

