edamame#
- edamame.eda
- eda
correlation_categorical()
correlation_pearson()
correlation_phik()
describe_distribution()
dimensions()
drop_columns()
handling_missing()
identify_types()
inspection()
interaction()
missing()
modify_cardinality()
num_to_categorical()
num_variable_study()
plot_categorical()
plot_numerical()
split_and_scaling()
view_cardinality()
- tools
- eda
- edamame.regressor
- diagnose
RegressorDiagnose
RegressorDiagnose.X_train
RegressorDiagnose.y_train
RegressorDiagnose.X_test
RegressorDiagnose.y_test
RegressorDiagnose.coefficients()
RegressorDiagnose.prediction_error()
RegressorDiagnose.qqplot()
RegressorDiagnose.random_forest_fi()
RegressorDiagnose.residual_plot()
RegressorDiagnose.xgboost_fi()
check_random_forest()
check_xgboost()
- regression
TrainRegressor
TrainRegressor.X_train
TrainRegressor.y_train
TrainRegressor.X_test
TrainRegressor.y_test
TrainRegressor.auto_ml()
TrainRegressor.lasso()
TrainRegressor.linear()
TrainRegressor.model_metrics()
TrainRegressor.random_forest()
TrainRegressor.ridge()
TrainRegressor.save_model()
TrainRegressor.tree()
TrainRegressor.xgboost()
regression_metrics()
- diagnose
- edamame.classifier
- classification
TrainClassifier
TrainClassifier.X_train
TrainClassifier.y_train
TrainClassifier.X_test
TrainClassifier.y_test
TrainClassifier.auto_ml()
TrainClassifier.gaussian_nb()
TrainClassifier.knn()
TrainClassifier.logistic()
TrainClassifier.model_metrics()
TrainClassifier.random_forest()
TrainClassifier.save_model()
TrainClassifier.svm()
TrainClassifier.tree()
TrainClassifier.xgboost()
classifier_metrics()
- diagnose
- classification