Hyperparameter Tuning

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Hyperparameter Tuning

Hyperparameter tuning is the process of finding the best combination of hyperparameters for a machine
learning model. Hyperparameters are settings that are specified before training the model and cannot
be learned from the data. They control the behavior of the model and affect its performance, such as
the learning rate, the number of hidden layers in a neural network, or the regularization parameter.

To find the optimal combination of hyperparameters, hyperparameter tuning techniques are employed.
One common approach is grid search, where a predefined set of hyperparameter values is defined, and
the model is trained and evaluated for each combination of values. The combination that produces the
best performance metric is selected as the optimal set of hyperparameters.

Let's consider an example to illustrate hyperparameter tuning

Suppose we have a support vector machine (SVM) model that we want to train on a dataset for image classification. The SVM has hyperparameters such as the kernel type, the value of C (regularization parameter), and the gamma parameter. We want to find the best combination of these hyperparameters.

In grid search, we would define a grid of possible values for each hyperparameter. For example, we
might define three kernel types (linear, polynomial, and radial basis function), three values for C (0.1, 1,
and 10), and two values for gamma (0.01 and 0.1). This results in a total of 3 x 3 x 2 = 18 combinations.

We then train and evaluate the SVM model for each combination of hyperparameters using a suitable
evaluation metric, such as accuracy or F1 score. The combination of hyperparameters that produces the
highest accuracy (or the desired metric) on a validation set is selected as the optimal set.

For instance, after training the SVM model with each combination of hyperparameters, we find that the
combination of the polynomial kernel, C=1, and gamma=0.1 achieves the highest accuracy. Thus, these
values are chosen as the optimal hyperparameters for the SVM model.

Hyperparameter tuning is crucial because selecting inappropriate hyperparameter values can result in
poor model performance or overfitting. By systematically searching for the best hyperparameters, we
can improve the model’s accuracy and generalization ability, leading to better results on unseen data.

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