Hyperparameter Tuning

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

Tuning hyperparameters is critical because it can significantly improve the model’s performance by striking the right balance between underfitting and overfitting. Proper hyperparameter selection enhances the model’s ability to generalize well to unseen data, ensuring robustness and reliability in practical applications. For example, if the learning rate is set too high, the model might miss the optimal solution, leading to underfitting, whereas a very low learning rate may cause the model to memorize the training data, resulting in overfitting. Similarly, hyperparameters like the regularization strength can control how much the model penalizes large coefficients, preventing it from relying too heavily on any single feature (avoiding overfitting) while still ensuring that meaningful patterns are captured (avoiding underfitting). By carefully tuning these parameters, the model can generalize ...