How can I effectively debug my machine learning model?

How can I effectively debug my machine learning model?

Debugging a machine learning model involves checking data quality, model assumptions, and performance metrics.

What are some common issues that can arise during debugging?

Common issues include overfitting, underfitting, data leakage, and incorrect feature selection.

How do I identify if my model is overfitting?

You can identify overfitting by comparing training and validation performance; a large gap indicates overfitting.

What tools can I use for debugging?

Tools like TensorBoard, MLflow, and various visualization libraries can help in debugging machine learning models.