Precision Recall F1

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score, confusion_matrix
X,y=load_breast_cancer(return_X_y=True); Xtr,Xte,ytr,yte=train_test_split(X,y,random_state=0)
g=GridSearchCV(RandomForestClassifier(random_state=0),{"n_estimators":[50,100],"max_depth":[3,None]},cv=3).fit(Xtr,ytr)
p=g.predict(Xte); print("Best:",g.best_params_); print(confusion_matrix(yte,p))
print(classification_report(yte,p)); print("ROC-AUC:",round(roc_auc_score(yte,g.predict_proba(Xte)[:,1]),3))