How to optimise hyper-parameters of a DecisionTree Model using GridSearchCV in Python

When building a machine learning model, it’s important to optimize the parameters of the model for the best performance. One way to do this is by tuning the hyper-parameters of a DecisionTree model using GridSearchCV.

A Decision Tree model is a simple and powerful algorithm that is used to make predictions by building a tree-like structure of decisions. The hyper-parameters of a DecisionTree model include the criterion (used to measure the quality of the split), max_depth (maximum depth of the tree), min_samples_split (minimum number of samples required to split an internal node), min_samples_leaf (minimum number of samples required to be at a leaf node), max_features (number of features to consider when looking for the best split), and splitter (strategy used to choose the split at each node).

GridSearchCV is a method that allows you to search for the best combination of hyper-parameters, by training and evaluating a model using different combinations of hyper-parameters. It will pick the best combination of hyper-parameters based on the performance of the model.

In Python, the library scikit-learn provides an easy way to perform GridSearchCV using the function GridSearchCV().

The first step is to import the library and load the dataset into a pandas dataframe. Then, split the data into training and testing sets, and create an instance of the DecisionTree model you want to evaluate.

After that, you can use the GridSearchCV() function, which takes the DecisionTree model, the dataset, a dictionary of hyper-parameters (criterion, max_depth, min_samples_split, min_samples_leaf, max_features, and splitter) and their possible values, and the scoring metric as inputs. The function returns the best combination of hyper-parameters based on the performance of the model.

 

In this Learn through Codes example, you will learn: How to optimise hyper-parameters of a DecisionTree Model using GridSearchCV in Python.