Hyperparameter Tuning
Explore how to optimize machine learning models by tuning hyperparameters that control learning behavior. Understand differences between hyperparameters and model parameters, methods including grid search and random search, and apply practical strategies for balancing bias and variance. Gain skills to enhance model performance and generalization through effective hyperparameter selection.
Hyperparameters are the settings that define how your model learns, not what it learns. Choosing them poorly can undermine your entire pipeline. In this lesson, we’ll walk through what tuning involves, popular tuning strategies, and how to apply them in practice. Let’s get started.
Basics of hyperparameter tuning
You’re asked to explain the concept of hyperparameter tuning during an ML system design round. The interviewer is looking for clarity on what makes hyperparameters different from model parameters and why tuning them is crucial.
Sample answer
Hyperparameter tuning involves optimizing the parameters that are not learned during the training process but are predefined before training begins. These include settings such as:
The learning rate, which controls the step size during optimization.
The number of trees in a random forest, which affects the ensemble size.
The regularization strength in a logistic regression model, which helps manage overfitting.
In contrast, model parameters are the values that the model learns directly from the training data during the training process. For example:
The weights in a neural network
The coefficients in a linear regression model
The splits in a decision tree ...