Hyperparameter Tuning: Finding Optimal Model Configuration

You built a model. It works. But is it optimized? Could different settings make it better?

Hyperparameters control how models learn. Tuning them — finding the best configuration — often makes the difference between mediocre and excellent performance.

What Are Hyperparameters?

A hyperparameter is a parameter whose value is used to control the training process.

This distinguishes them from model parameters, which the training process learns automatically. You set hyperparameters before training starts. The model learns parameters during training.

Examples Across Model Types

Random Forest:

• Maximum depth of trees: How deep can each decision tree grow?
• Number of trees: How many trees in the forest?
• Number of features: How many features to consider at each split?

Linear Regression:

• Normalize features or not: Should features be scaled to similar ranges?
• Regularization strength: How much to penalize large coefficients?

Neural Networks:

• Activation function: ReLU, sigmoid, tanh — which nonlinearity?
• Number of neurons in each hidden layer: Network width and depth
• Learning rate: How big are gradient descent…