Introduction to Hyperparameters

Introduction to hyperparameters

An ML model has two types of parameters: hyperparameters and trainable parameters after training the model.

Parameters are values that are learned by the ML model during training. Examples of parameters include the coefficients in a linear regression model or the decision tree split points in a decision tree. During the training process, these parameters are adjusted iteratively until the ML model’s performance is optimized and the error between predicted output and actual output is minimal.

Hyperparameters are different parameter values that are set before starting the training process for an ML model. The main function of the hyperparameters is to control the learning process. They have a significant effect on the performance of ML models.

Examples of hyperparameters

Some examples of hyperparameters in ML algorithms include:

• Regularization strength: This controls the amount of regularization applied to the ML model, which helps prevent overfitting.

• Number of trees in a random forest: A larger number of trees can lead to better ML model performance. It can also increase the risk of overfitting due to the depth of the trees. ...