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from sklearn import svm

from sklearn.ensemble import BaggingClassifier

from sklearn.ensemble import GradientBoostingClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import log_loss

from sklearn.neighbors import KNeighborsClassifier

clf = RandomForestClassifier(n_estimators=1500)
clf.fit(X_train, y_train)
y_val_pred = clf.predict_proba(X_val)
log_loss(y_val, y_val_pred)

from sklearn.ensemble import BaggingClassifier
b1 = BaggingClassifier(n_estimators=2000)
b1.fit(X_train, y_train)
y_val_pred = b1.predict_proba(X_val)
log_loss(y_val, y_val_pred)

from sklearn.ensemble import GradientBoostingClassifier
gbc = GradientBoostingClassifier()
gbc.fit(X_train, y_train)
y_val_pred = gbc.predict_proba(X_val)
log_loss(y_val, y_val_pred)

from sklearn import svm
clf = svm.SVC()
clf.fit(X_train, y_train)
y_val_pred = clf.predict_proba(X_val)
log_loss(y_val, y_val_pred)

from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X_train, y_train)
y_val_pred = neigh.predict_proba(X_val)
log_loss(y_val, y_val_pred)