∇Training & optimization

See how models adjust values through feature engineering, loss functions, gradient descent, and learning rates. — 5 entries

Create useful model inputs from raw data.

Convert prediction errors into the quantity training minimizes.

Repeatedly update parameters in the direction that lowers loss.

Set the step size of each gradient update.

Penalize complexity to discourage extreme fitted coefficients.