from sklearn.datasets import load_wine
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
X_train, X_test, y_train, y_test = train_test_split(data.data,
params = {'n_estimators': 10,
clf = RandomForestClassifier(**params)
clf.fit(X_train, y_train)
y_train_pred = clf.predict_proba(X_train)
y_test_pred = clf.predict_proba(X_test)
train_f1 = f1_score(y_train, y_train_pred.argmax(axis=1), average='macro')
test_f1 = f1_score(y_test, y_test_pred.argmax(axis=1), average='macro')
print(f'Train f1:{train_f1} | Test f1:{test_f1}')