to_drop = ["ht", "at", "Unnamed: 0"] results = "home_team_won" clf = DecisionTreeClassifier(criterion="gini") data = os.path.abspath("./data/big_data.csv") clf = Model(clf, data) clf.drop_columns(to_drop) clf.get_X_y(results) #tester = Test(clf) #tester.test_k_best() #clf.feature_corr() clf.k_best(chi2, 33) clf.split_data(0.2, 42) #clf.standard_scale() #clf.lda(1) #clf.rfe(10) clf.fit_clf() clf.pred_clf() ac, cm, cr = clf.eval_clf() fi = clf.feature_importances() print(fi) print(cm) print(cr) print(ac)