/Bagging-and-Random-Forest-implementation

Bagging and Random Forest implementation using Machine Learning

Primary LanguageJupyter Notebook

Bagging and Random Forest implementation using Machine Learning

1. Bagging implementation on breast_cancer Dataset

2. Random Forest implementation on Wine-Quality Dataset

  • Hyperparameters of Decision tree:

class_weight=None, criterion='entropy', max_depth=24,max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, presort=False, random_state=None, splitter='best'

  • Hyperparameters of Bagging classifier:

base_estimator=None, bootstrap=True, bootstrap_features=False, max_features=1.0, max_samples=1.0, n_estimators=10, n_jobs=None, oob_score=False, random_state=None, verbose=0, warm_start=False

  • Hyperparameters of Random forest classifier:

bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None, oob_score=False, random_state=None, verbose=0, warm_start=False