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from sklearn import tree

from sklearn.ensemble import GradientBoostingClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import classification_report

from sklearn.neural_network import MLPClassifier

clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

clf = MLPClassifier(hidden_layer_sizes=(50, 100, 200, 400, 800, 1600, 3200, 1600, 800, 400, 200, 100, 50), max_iter=3000, random_state=42)
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

clf = RandomForestClassifier(max_depth=2000)
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

clf = GradientBoostingClassifier(n_estimators=300, max_depth=300, random_state=42)
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))
print(clf.score(X_test, y_test))