Hyperparameter Tuning in Machine Learning
Introduction
Hyperparameter tuning is a crucial step in optimizing machine learning models. Hyperparameters are external configurations that control the learning process and affect the performance of models. Unlike model parameters (such as weights in neural networks or coefficients in linear regression), hyperparameters are set before training and do not change during training.
Proper hyperparameter tuning enhances model accuracy, generalization, and computational efficiency. In this guide, we will cover:
✔ What hyperparameters are and why they are important
✔ Different methods for hyperparameter tuning
✔ Common hyperparameters for various algorithms
✔ Practical implementations in Python
What are Hyperparameters?
Hyperparameters are configuration settings set before training a machine learning model. They influence the learning process and model performance. Some common hyperparameters include:
- Learning rate (α\alpha) – Controls how much weights are updated during training.
- Number of hidden layers and neurons (Neural Networks) – Determines model complexity.
- Number of trees (Random Forest, Gradient Boosting) – Affects model strength and overfitting.
- Kernel type (SVM) – Defines decision boundary for classification tasks.
- Regularization parameter (λ\lambda) – Controls overfitting by penalizing complex models.
Hyperparameters vs Parameters
Feature | Hyperparameters | Parameters |
---|---|---|
Definition | Configurations set before training | Learned from training data |
Examples | Learning rate, Number of trees, Kernel type | Weights in neural networks, Coefficients in linear regression |
Adjusted? | Manually or through tuning | Learned by the algorithm |
Optimization method | Grid Search, Random Search, Bayesian Optimization | Gradient Descent, Backpropagation |
Why is Hyperparameter Tuning Important?
Hyperparameter tuning helps achieve better accuracy, generalization, and efficiency.
🚀 Optimized Performance – The right hyperparameters improve model accuracy.
⚡ Faster Convergence – Correct settings ensure the model learns efficiently.
🔍 Better Generalization – Avoids overfitting and underfitting.
💾 Computational Efficiency – Prevents unnecessary training iterations.
Common Hyperparameters for Different Algorithms
Algorithm | Important Hyperparameters |
---|---|
Linear Regression | Regularization parameter (Lasso, Ridge) |
Logistic Regression | Regularization strength (C), Learning rate |
Decision Trees | Maximum depth, Minimum samples per split, Split criterion |
Random Forest | Number of trees, Maximum depth, Minimum samples per leaf |
Support Vector Machines (SVM) | Kernel type, Regularization parameter (C), Gamma |
K-Nearest Neighbors (KNN) | Number of neighbors (K), Distance metric |
Gradient Boosting (XGBoost, LightGBM, CatBoost) | Learning rate, Number of estimators, Max depth, Subsampling rate |
Neural Networks (Deep Learning) | Learning rate, Number of layers, Number of neurons, Dropout rate, Batch size |
Hyperparameter Tuning Methods
There are several approaches to finding the best hyperparameters for a model.
1. Manual Tuning (Trial and Error)
- Adjusting hyperparameters based on experience.
- Used when we have domain expertise.
- Not efficient for complex models.
👎 Cons: Time-consuming, suboptimal results.
2. Grid Search
- Exhaustively tries all possible hyperparameter combinations.
- Uses cross-validation to evaluate each combination.
Example
For an SVM model, if we test:
- Kernel: [‘linear’, ‘rbf’]
- C: [0.1, 1, 10]
It tests 2×3=62 \times 3 = 6 models.
Python Implementation
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
# Define model and parameters
model = SVC()
param_grid = {
'kernel': ['linear', 'rbf'],
'C': [0.1, 1, 10]
}
# Perform Grid Search
grid_search = GridSearchCV(model, param_grid, cv=5)
grid_search.fit(X_train, y_train)
# Best parameters
print("Best Hyperparameters:", grid_search.best_params_)
👍 Pros: Finds best combination.
👎 Cons: Computationally expensive for large datasets.
3. Random Search
- Tests random combinations instead of all possibilities.
- Faster than Grid Search.
Python Implementation
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import uniform
# Define model and parameters
param_dist = {
'C': uniform(0.1, 10),
'kernel': ['linear', 'rbf']
}
# Perform Random Search
random_search = RandomizedSearchCV(SVC(), param_dist, n_iter=5, cv=5, random_state=42)
random_search.fit(X_train, y_train)
# Best parameters
print("Best Hyperparameters:", random_search.best_params_)
👍 Pros: Faster than Grid Search.
👎 Cons: Might miss the best combination.
4. Bayesian Optimization
- Uses probabilistic models to find the best hyperparameters.
- More efficient than Grid and Random Search.
Python Implementation (Using Optuna)
import optuna
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
# Define optimization function
def objective(trial):
C = trial.suggest_float("C", 0.1, 10)
kernel = trial.suggest_categorical("kernel", ["linear", "rbf"])
model = SVC(C=C, kernel=kernel)
score = cross_val_score(model, X_train, y_train, cv=5).mean()
return score
# Run optimization
study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=20)
print("Best Hyperparameters:", study.best_params_)
👍 Pros: Smart selection of hyperparameters.
👎 Cons: Requires more expertise to set up.
5. Genetic Algorithms (Evolutionary Algorithms)
- Mimics natural selection.
- Uses mutation, crossover, and selection to find the best hyperparameters.
Python Implementation (Using TPOT)
from tpot import TPOTClassifier
# Define TPOT model
tpot = TPOTClassifier(generations=5, population_size=20, cv=5, verbosity=2)
tpot.fit(X_train, y_train)
# Best pipeline
print(tpot.fitted_pipeline_)
👍 Pros: Automates hyperparameter search.
👎 Cons: Computationally expensive.
Comparison of Hyperparameter Tuning Methods
Method | Accuracy | Speed | Best for Large Datasets? |
---|---|---|---|
Manual Tuning | Low | Fast | ✅ Yes |
Grid Search | High | Slow | ❌ No |
Random Search | Medium | Faster than Grid | ✅ Yes |
Bayesian Optimization | High | Fast | ✅ Yes |
Genetic Algorithms | High | Slow | ❌ No |
Best Practices for Hyperparameter Tuning
✔ Start with default values and check model performance.
✔ Use Random Search for quick tuning, then refine with Grid Search or Bayesian Optimization.
✔ Consider computational cost – avoid exhaustive searches for large datasets.
✔ Use Cross-Validation to evaluate hyperparameter performance.
✔ Monitor model overfitting – tune regularization parameters accordingly.