Hyperparameter Tuning

TL;DR

Adjusts a model’s hyperparameters to improve performance for a given dataset.
Common methods include manual tuning, grid search, random search, and Bayesian optimization.
Method choice affects computational cost and likelihood of finding an optimal configuration.

Definition

Hyperparameter tuning is the process of adjusting the hyperparameters of a machine learning model to optimize its performance on a specific dataset.

Explanation

Hyperparameter tuning is essential to achieving the best possible results from a model. Hyperparameters are settings external to the model parameters learned during training; changing them alters model behavior and performance. Methods for tuning described in the source are:

Manual tuning: The practitioner manually adjusts hyperparameters based on knowledge and experience. It can be effective with deep understanding but is labor-intensive and time-consuming.
Grid search: Specify a range of values for each hyperparameter and search through all possible combinations to find the best set. It can be effective but is often computationally expensive and may not find the optimal set.
Random search: Randomly sample hyperparameter values from specified ranges. It can be less computationally expensive than grid search but may not always find the optimal set.
Bayesian optimization: Use Bayesian statistics to model performance as a function of hyperparameters and guide search. It can be effective but can be computationally expensive and may not suit all models and datasets.

Examples

Deep learning (convolutional neural networks)

In deep learning, tuning the hyperparameters of a neural network optimizes its performance on a particular dataset. For a convolutional neural network, hyperparameters might include the number of layers, the size of the filters, the stride length, and the activation function. By tuning these hyperparameters, a practitioner can improve the accuracy and efficiency of the network on a specific dataset.

Decision trees

For decision trees, hyperparameters include the maximum depth of the tree, the minimum number of samples required at a leaf node, and the minimum number of samples required to split an internal node. Adjusting these hyperparameters can improve the accuracy and efficiency of the decision tree on a particular dataset.

Notes or pitfalls

Manual tuning is labor-intensive and time-consuming, though potentially effective with deep domain knowledge.
Grid search can be computationally expensive and may not always find the optimal hyperparameter combination.
Random search may be less computationally expensive than grid search but may also fail to find the optimal set.
Bayesian optimization can be effective but may be computationally expensive and not suitable for all models and datasets.

Hyperparameters
Manual tuning
Grid search
Random search
Bayesian optimization
Neural network
Convolutional neural network
Decision tree