f1 score model implementation

# Import Libraries
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.ensemble import RandomForestClassifier

# Feature Engineering
y=df.pop('class')
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.1, random_state=42)

# Model Implementation
clf = RandomForestClassifier()
clf = clf.fit(X_train, y_train)
predictions=clf.predict(X_test)
print(f1_score(predictions,y_test))