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.
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.
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.