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Hyperparameter Tuning and Model Selection
Learn how to tune hyperparameters and choose the best model for a given problem
Hyperparameter tuning and model selection are crucial steps in the machine-learning pipeline. These processes help to optimize the performance of the model by finding the best combination of hyperparameters and choosing the appropriate model for the problem at hand. In this article, we will discuss hyperparameter tuning and model selection in detail.
Hyperparameter Tuning
Hyperparameters are the parameters of a machine learning model that are set before training the model. They control the behavior and performance of the model. Examples of hyperparameters include the learning rate in gradient descent, the regularization coefficient, and the number of hidden layers in a neural network.
Hyperparameter tuning refers to the process of finding the best combination of hyperparameters that leads to the best performance of the model. There are several ways to perform hyperparameter tuning:
Grid Search
Grid search involves exhaustively searching over a pre-defined set of hyperparameters. For example, if we have two hyperparameters, learning rate, and regularization coefficient, we can specify a grid of…