generating three models, training them on the make_moons dataset, then evaluating their accuracy

three_models.py

# Creating three models with three different algorithms
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC

tree_clf = DecisionTreeClassifier(random_state=42)
log_clf = LogisticRegression(solver="lbfgs", random_state=42)
svm_clf = SVC(gamma="scale", random_state=42)

# Training, predicting, then evaluating the predictions
# of all three models
from sklearn.metrics import accuracy_score

for clf in (tree_clf, log_clf, svm_clf):
    clf.fit(X_train, y_train) # training
    y_pred = clf.predict(X_test) # predicting
    print(clf.__class__.__name__, accuracy_score(y_test, y_pred)) # evaluating
    
# Output of the evaluation:
# DecisionTreeClassifier 0.856
# LogisticRegression 0.864
# SVC 0.896