How to select model using Grid Search in Python

Hits: 54

How to select model using Grid Search in Python

When building a machine learning model, it’s important to pick the right model that works best for the dataset. One way to do this is by using grid search to compare different models.

Grid search is a method that allows you to evaluate multiple models with different hyper-parameters, and select the one that has the best performance. This method tests all possible combinations of hyper-parameters for each model, and returns the best model based on a specific scoring metric.

In Python, the library scikit-learn provides an easy way to perform grid search 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 models you want to evaluate.

After that, you can use the GridSearchCV() function, which takes the list of models, the dataset, a dictionary of hyper-parameters and their possible values, and the scoring metric as inputs. The function returns the best model based on the performance of the model.

You can use the cv parameter that takes the number of splits you would like to make, or an iterable that you can use to define the splits.

Additionally, you can use the refit parameter, it will refit the estimator with best hyper-parameters using all available data.

In summary, grid search is a powerful tool for selecting the best model for a dataset. By using the GridSearchCV function in scikit-learn, it’s easy to compare different models in Python, making it a valuable tool for data scientists and machine learning practitioners.


In this Learn through Codes example, you will learn: How to select model using Grid Search in Python.


Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

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.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners