How can I evaluate my machine learning model using scikit-learn?

How can I evaluate my machine learning model using scikit-learn?

You can evaluate your model using various metrics like accuracy, precision, recall, and F1 score. Would you like to see an example?

Yes, please provide an example of how to calculate accuracy.

Sure! You can use the accuracy_score function from sklearn.metrics. After making predictions, you can compare them to the true labels like this: accuracy_score(y_true, y_pred).

What about confusion matrix? How do I use that?

You can use confusion_matrix from sklearn.metrics to visualize the performance. It shows true positives, false positives, true negatives, and false negatives.

Can you explain precision and recall?

Absolutely! Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances. They are crucial for imbalanced datasets.