LinearDiscriminantAnalysis()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearDiscriminantAnalysis()
StackingClassifier(estimators=[('svm',\n",
" SVC(C=0.1, gamma='auto', kernel='poly',\n",
" probability=True)),\n",
" ('xgb',\n",
" XGBClassifier(base_score=None, booster=None,\n",
" callbacks=None,\n",
" colsample_bylevel=None,\n",
" colsample_bynode=None,\n",
" colsample_bytree=0.4,\n",
" early_stopping_rounds=None,\n",
" enable_categorical=False,\n",
" eval_metric=None,\n",
" feature_types=None, gamma=0,\n",
" gpu_id=None, grow_policy=None,...\n",
" n_estimators=100, n_jobs=None,\n",
" num_parallel_tree=None,\n",
" predictor=None, random_state=None, ...)),\n",
" ('lr',\n",
" LogisticRegression(C=0.22564631610840102,\n",
" max_iter=2391,\n",
" solver='newton-cg')),\n",
" ('rf',\n",
" RandomForestClassifier(max_depth=5,\n",
" max_features=5,\n",
" min_samples_leaf=5,\n",
" min_samples_split=10,\n",
" n_estimators=20,\n",
" random_state=42))],\n",
" final_estimator=LogisticRegression(max_iter=3000))In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. StackingClassifier(estimators=[('svm',\n",
" SVC(C=0.1, gamma='auto', kernel='poly',\n",
" probability=True)),\n",
" ('xgb',\n",
" XGBClassifier(base_score=None, booster=None,\n",
" callbacks=None,\n",
" colsample_bylevel=None,\n",
" colsample_bynode=None,\n",
" colsample_bytree=0.4,\n",
" early_stopping_rounds=None,\n",
" enable_categorical=False,\n",
" eval_metric=None,\n",
" feature_types=None, gamma=0,\n",
" gpu_id=None, grow_policy=None,...\n",
" n_estimators=100, n_jobs=None,\n",
" num_parallel_tree=None,\n",
" predictor=None, random_state=None, ...)),\n",
" ('lr',\n",
" LogisticRegression(C=0.22564631610840102,\n",
" max_iter=2391,\n",
" solver='newton-cg')),\n",
" ('rf',\n",
" RandomForestClassifier(max_depth=5,\n",
" max_features=5,\n",
" min_samples_leaf=5,\n",
" min_samples_split=10,\n",
" n_estimators=20,\n",
" random_state=42))],\n",
" final_estimator=LogisticRegression(max_iter=3000))SVC(C=0.1, gamma='auto', kernel='poly', probability=True)
XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
" colsample_bylevel=None, colsample_bynode=None,\n",
" colsample_bytree=0.4, early_stopping_rounds=None,\n",
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
" gamma=0, gpu_id=None, grow_policy=None, importance_type=None,\n",
" interaction_constraints=None, learning_rate=0.001, max_bin=None,\n",
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
" max_delta_step=None, max_depth=8, max_leaves=None,\n",
" min_child_weight=30, missing=nan, monotone_constraints=None,\n",
" n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
" predictor=None, random_state=None, ...)LogisticRegression(C=0.22564631610840102, max_iter=2391, solver='newton-cg')
RandomForestClassifier(max_depth=5, max_features=5, min_samples_leaf=5,\n",
" min_samples_split=10, n_estimators=20, random_state=42)LogisticRegression(max_iter=3000)