How do I evaluate a machine learning model in Python?

How do I evaluate a machine learning model in Python?

To evaluate a machine learning model in Python, you can use libraries like scikit-learn. Start by splitting your data into training and testing sets.

What metrics should I use for evaluation?

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

Can you show me an example of using scikit-learn for evaluation?

Sure! Here's a simple example:

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier

# Sample data
X, y = load_data()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = RandomForestClassifier()
model.fit(X_train, y_train)

predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f'Accuracy: {accuracy}')