How can I compare different machine learning models automatically in Python?

How can I compare different machine learning models automatically in Python?

You can use libraries like scikit-learn and AutoML tools to automate model comparison. Would you like a code example?

Yes, please provide a code example for comparing models.

Here's a simple example using scikit-learn:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load dataset
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define models
models = {
    'Random Forest': RandomForestClassifier(),
    'SVM': SVC(),
    'Logistic Regression': LogisticRegression()
}

# Compare models
results = {}
for name, model in models.items():
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)
    results[name] = accuracy

print(results)

This will give you the accuracy of each model.

What are some metrics I should consider when comparing models?

Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC. The choice depends on your specific problem.