The Regression class simplifies regression analysis by providing a convenient and flexible approach for model training, evaluation, and hyperparameter tuning.The Classifier class streamlines classification tasks by offering a well-organized framework for model selection, hyperparameter tuning,
Project description
Project Title
Simplifying Regression and Classification Modeling
Guide
Installation setup
pip install AlgoMaster
Classfication model
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Initialize the model
`Classifier=AlgoMaster.Classifier(X,Y,test_size=0.2,random_state=20)`
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Train the model and predict the results in table format
`Classifier.model_training()`
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Ensemble technique
`Classifier.ensemble_prediction(No. of models)`
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Single Training
To predict unseen data
`data=[1,2,3,4,5,6,7,8,9] Classifier.logistic_test(data) Classifier.KNeighbors_test(data) Classifier.GaussianNB_test(data) Classifier.Bagging_test(data) Classifier.ExtraTrees_test(data) Classifier.RandomForest_test(data) Classifier.DecisionTree_test(data) Classifier.AdaBoost_test(data) Classifier.GradientBoosting_test(data) Classifier.XGBoost_test(data) Classifier.SGD_test(data) Classifier.SVC_test(data) Classifier.Ridge_test(data) Classifier.BernoulliNB_test(data)`
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Hyperparameter Turning
To find the best parameters for the model
`Classifier.hyperparameter_tuning()`
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Single Hyperparameter Turning
To find the best parameters for the model
`Classifier.logistic_hyperparameter() Classifier.KNeighbors_hyperparameter() Classifier.GaussianNB_hyperparameter() Classifier.Bagging_hyperparameter() Classifier.ExtraTrees_hyperparameter() Classifier.RandomForest_hyperparameter() Classifier.DecisionTree_hyperparameter() Classifier.AdaBoost_hyperparameter() Classifier.GradientBoosting_hyperparameter() Classifier.XGBoost_hyperparameter() Classifier.SGD_hyperparameter() Classifier.SVC_hyperparameter() Classifier.Ridge_hyperparameter() Classifier.BernoulliNB_hyperparameter()`
Regression model
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Initialize the model
`Regressor=AlgoMaster.Regressor(X,Y,test_size=0.2,random_state=20)`
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Train the model and predict the results in table format
`Regressor.model_training()`
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Ensemble technique
`Regressor.ensemble_prediction(No. of models)`
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Single Training
`data=[1,2,3,4,5,6,7,8,9] Regressor.LinearRegression_test(data) Regressor.KNeighbors_test(data) Regressor.Bagging_test(data) Regressor.ExtraTrees_test(data) Regressor.RandomForest_test(data) Regressor.DecisionTree_test(data) Regressor.AdaBoost_test(data) Regressor.GradientBoosting_test(data) Regressor.XGBoost_test(data) Regressor.TheilSen_test(data) Regressor.SVR_test(data) Regressor.Ridge_test(data) Regressor.RANSAC_test(data) Regressor.ARD_test(data) Regressor.BayesianRidge_test(data) Regressor.HuberRegressor_test(data) Regressor.Lasso_test(data) Regressor.ElasticNet_test(data)`
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Hyperparameter Turning
To find the best parameters for the model
`Regressor.hyperparameter_tuning()`
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Single Hyperparameter Turning
To find the best parameters for the model
`Regressor.KNeighbors_hyperparameter() Regressor.Bagging_hyperparameter() Regressor.ExtraTrees_hyperparameter() Regressor.RandomForest_hyperparameter() Regressor.DecisionTree_hyperparameter() Regressor.AdaBoost_hyperparameter() Regressor.GradientBoosting_hyperparameter() Regressor.XGBoost_hyperparameter() Regressor.TheilSen_hyperparameter() <!-- Regressor.SVR_hyperparameter() --> Regressor.Ridge_hyperparameter() Regressor.RANSAC_hyperparameter() Regressor.ARD_hyperparameter() Regressor.BayesianRidge_hyperparameter() Regressor.Lasso_hyperparameter() Regressor.ElasticNet_hyperparameter()`
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