src/theory/hyperparameter_tuning.qmd

# Understanding Hyperparameter Tuning

Hyperparameters are crucial parameters that define a machine learning model's behavior during training. They play an essential role in determining how well a model learns from data and generalizes to unseen examples. In this article, we will explore the concept of hyperparameter tuning, its importance, and various techniques used for optimizing these parameters.

## What are Hyperparameters?

Hyperparameters are settings or configurations that control the learning process in a machine learning model. They differ from model parameters (weights) as they cannot be learned directly from data during training. Instead, hyperparameters must be set beforehand and remain constant throughout the training process. Some common examples of hyperparameters include:

- Learning rate
- Number of hidden layers and neurons in a neural network
- Kernel type and regularization parameters for Support Vector Machines (SVM)
- Tree depth or number of trees in ensemble methods like Random Forest or Gradient Boosting

## Why is Hyperparameter Tuning Important?

Hyperparameters significantly impact the performance of machine learning models. Properly tuned hyperparameters can lead to better model accuracy, faster convergence during training, and improved generalization on unseen data. On the other hand, poorly chosen hyperparameters may result in underfitting or overfitting issues, leading to suboptimal predictions.

## Hyperparameter Tuning Techniques

There are several techniques available for optimizing hyperparameters:

1. Grid Search
2. Random Search
3. Bayesian Optimization
4. Gradient-based optimization
5. Evolutionary Algorithms
6. Population Based Training (PBT)

### 1. Grid Search

Grid search is a brute force approach that exhaustively searches through all possible combinations of hyperparameters within predefined ranges or values. It evaluates the model's performance for each combination and selects the best one based on a chosen metric, such as accuracy or loss.

```python
from sklearn.model_selection import GridSearchCV
# Define parameter grid
param_grid = {
    'learning_rate': [0.1, 0.01, 0.001],
    'max_depth': [3, 5, 7]
}
# Create a model instance and perform GridSearchCV
model = SomeModel()
grid_search = GridSearchCV(estimator=model, param_grid=param_grid)
```

### 2. Random Search

Random search is an alternative to grid search that randomly samples hyperparameter combinations from a predefined distribution or range. It can be more efficient than grid search when the number of hyperparameters and their possible values are large.

```python
from sklearn.model_selection import RandomizedSearchCV
# Define parameter distributions
param_distributions = {
    'learning_rate': [0.1, 0.01, 0.001],
    'max_depth': [3, 5, 7]
}
# Create a model instance and perform RandomizedSearchCV
model = SomeModel()
random_search = RandomizedSearchCV(estimator=model, param_distributions=param_distributions)
```

### 3. Bayesian Optimization

Bayesian optimization is an approach that uses a probabilistic model to estimate the performance of hyperparameter combinations and select new ones based on this estimation. It can be more efficient than grid or random search, especially when evaluating each combination's cost (e.g., time) is high.

```python
from skopt import BayesSearchCV
# Define parameter space
param_space = [
    Real(0.1, 0.3, name='learning_rate'),
    Integer(2, 8, name='max_depth')
]
# Create a model instance and perform BayesSearchCV
model = SomeModel()
bayes_search = BayesSearchCV(estimator=model, search_spaces=param_space)
```

### Conclusion

Hyperparameter tuning is an essential step in building effective machine learning models. By using techniques like grid search, random search, or Bayesian optimization, we can find the best hyperparameters for our model and improve its performance on unseen data.