Hyperparameter Tuning of Decision Tree Classifier Using GridSearchCV

Hyperparameter Tuning of Decision Tree Classifier Using GridSearchCV

We use the error component for each model. We select the hyperparameter that minimizes the error or maximizes the score on the validation set. In ending test our model performance using the test data. Below we are going to implement hyperparameter tuning using the sklearn library called gridsearchcv in Python.

The models can have many hyperparameters and finding the best combination of the parameter using grid search methods.

Grid search is a technique for tuning hyperparameter that may facilitate build a model and evaluate a model for every combination of algorithms parameters per grid.

We might use 10 fold cross-validation to search the best value for that tuning hyperparameter. Parameters like in decision criterion, max_depth, min_sample_split, etc. These values are called hyperparameters. To get the simplest set of hyperparameters we will use the Grid Search method. In the Grid Search, all the mixtures of hyperparameters combinations will pass through one by one into the model and check the score on each model. It gives us the set of hyperparameters which gives the best score. Scikit-learn package as a means of automatically iterating over these hyperparameters using cross-validation. This method is called Grid Search.

How does it work?

Grid Search takes the model or objects you’d prefer to train and different values of the hyperparameters. It then calculates the error for various hyperparameter values, permitting you to choose the best values.

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To illustrate an example of grid search how it works | Image: Source: Image created by the author.

Let the tiny circles represent different hyperparameters. We begin with one value for hyperparameters and train the model. We use different hyperparameters to train the model. We tend to continue the method until we have exhausted the various parameter values. Every model produces an error. We pick the hyperparameter that minimizes the error. To pick the hyperparameter, we split our dataset into 3 parts, the training set, validation set, and test set. We tend to train the model for different hyperparameters. We use the error component for each model. We select the hyperparameter that minimizes the error or maximizes the score on the validation set. In ending test our model performance using the test data.

Below we are going to implement hyperparameter tuning using the sklearn library called gridsearchcv in Python.

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