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 description
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
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