Hyperparameter tuning is a critical process in machine learning that involves selecting the optimal configuration settings (hyperparameters) to maximize a model’s performance. Unlike model parameters learned during training, hyperparameters are set before training and influence how the model learns. Proper tuning helps avoid underfitting or overfitting and improves generalization.
Introduction to Hyperparameter Tuning
Hyperparameters control various aspects of model training, such as learning rate, number of trees in an ensemble, or regularization strengths. Searching for the best combination is often a complex optimization problem, especially as models and parameter spaces grow larger. Hyperparameter tuning automates this search, balancing thoroughness and computational efficiency to find near-optimal configurations.
1. Grid Search
Grid Search performs an exhaustive search over a manually specified set of hyperparameter values.
How it Works:
1. Define discrete sets of possible values for each hyperparameter.
2. Train and evaluate the model for every combination using techniques like cross-validation.
3. Select the configuration yielding the best performance metric..png)
Use Cases: Small hyperparameter spaces with limited parameter ranges. When computational resources are sufficient.
2. Random Search
Random Search selects hyperparameter combinations randomly from predefined distributions without exhaustively testing every option.
How it Works:
1. Specify possible ranges or distributions for each hyperparameter.
2. Randomly sample sets of hyperparameters and evaluate performance.
3. Choose the best-performing set after a fixed number of trials.
.png)
Use Cases: Large hyperparameter spaces with unknown importance of parameters. Limited computational budget.
3. Bayesian Optimization
Bayesian Optimization uses probabilistic models to predict promising hyperparameters based on past evaluations, balancing exploration and exploitation.
How it Works:
1. Builds a surrogate model (e.g., Gaussian Process or Tree-structured Parzen Estimator) of the performance surface.
2. Uses acquisition functions to decide which hyperparameter set to evaluate next.
3. Updates the surrogate model iteratively with new observations.
4. Efficiently searches the space to find better hyperparameters with fewer evaluations.
.png)
Use Cases: Deep learning models with many hyperparameters and costly training. Situations where minimizing the evaluation count is crucial.
Class Sessions
Sales Campaign
We have a sales campaign on our promoted courses and products. You can purchase 1 products at a discounted price up to 15% discount.