AI & ML Crash Course: Hyperparameter Tuning & Regularization

1. Hyperparameters

Hyperparameters are parameters whose values are set before the learning process begins. They control the training process and the complexity of the model.

• Learning Rate
• Number of Epochs
• Batch Size
• Number of Hidden Layers
• Regularization Parameters

2. Hyperparameter Tuning

Hyperparameter tuning is the process of optimizing the hyperparameters of a model to achieve the best performance.

Common Methods for Hyperparameter Tuning

1. Grid Search
2. Random Search
3. Bayesian Optimization
4. Automated Machine Learning (AutoML)

Grid Search Example

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

# Define the model
model = RandomForestClassifier()

# Define the hyperparameters to tune
param_grid = {
    'n_estimators': [10, 50, 100],
    'max_depth': [None, 10, 20, 30]
}

# Setup the grid search
grid_search = GridSearchCV(model, param_grid, cv=5)

# Fit the model
grid_search.fit(X_train, y_train)

# Best parameters found
print(grid_search.best_params_)

3. Regularization

Regularization is a technique used to prevent overfitting by adding a penalty to the loss function.

Common Regularization Techniques

• L1 Regularization (Lasso)
• L2 Regularization (Ridge)
• Dropout (for neural networks)

L2 Regularization Example

from sklearn.linear_model import Ridge

# Create a Ridge regression model with L2 regularization
model = Ridge(alpha=1.0)

# Fit the model
model.fit(X_train, y_train)

4. Best Practices

Follow these best practices to effectively tune hyperparameters and regularization:

• Use cross-validation for reliable evaluation.
• Start with a wide range of hyperparameters, then narrow down.
• Monitor the performance on a validation set.
• Regularize your models to avoid overfitting.

5. FAQ