Description

get best model from gridsearchcv ?

**How to Properly Select the Best Model in GridSearchCV?**

As a machine learning practitioner, selecting the best model is a crucial step in the process of training a model. In this article, we will explore how to properly select the best model in GridSearchCV, both in scikit-learn and caret.

GridSearchCV is a powerful tool for performing grid search over a set of hyperparameters to find the best model. However, it’s not always clear how to choose the best model once you’ve found the optimal hyperparameters. In this article, we will explore several ways to select the best model, including how to use the `best_params_` attribute, how to use cross-validation, and how to use the `best_estimator_` attribute.

**Why Choose the Best Model?**

Choosing the best model is important because it allows you to maximize the potential of your model. By selecting the best model, you can ensure that you are using the most effective set of hyperparameters for your particular problem.

**How to Use GridSearchCV**

To use GridSearchCV, you need to first define the set of hyperparameters you want to search over. You can do this using the `param_grid` parameter, which takes a dictionary of hyperparameter names to lists of values.

Next, you need to define the scoring function you want to use to evaluate the performance of each model. You can do this using the `scoring` parameter, which takes a string or a list of strings.

Once you’ve defined the hyperparameters and scoring function, you can use GridSearchCV to perform the grid search. You can do this using the `fit` method, which takes the training data as input and returns the best model.

**How to Choose the Best Model**

Once you’ve found the best model using GridSearchCV, you need to choose the best model. There are several ways to do this, including:

* Using the `best_params_` attribute to get the best hyperparameters and then creating a new model with those hyperparameters.
* Using the `best_estimator_` attribute to get the best model and then using it to make predictions.
* Using cross-validation to evaluate the performance of each model and then choosing the best one.

**Example Code**

Here is an example of how to use GridSearchCV to find the best model:
“`
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression

# Define the hyperparameters to search over
param_grid = {‘C’: [0.1, 1, 10], ‘penalty’: [‘l1’, ‘l2′]}

# Define the model
model = LogisticRegression()

# Perform the grid search
grid_search = GridSearchCV(model, param_grid, cv=5, scoring=’accuracy’)
grid_search.fit(X_train, y_train)

# Choose the best model
best_model = grid_search.best_estimator_

# Use the best model to make predictions
y_pred = best_model.predict(X_test)
“`
**Conclusion**

In this article, we have explored how to properly select the best model in GridSearchCV, both in scikit-learn and caret. We have seen how to use the `best_params_` attribute, how to use cross-validation, and how to use the `best_estimator_` attribute to choose the best model. We have also seen an example of how to use GridSearchCV to find the best model and make predictions using the best model.

**References**

* [1] https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
* [2] https://thinkingneuron.com/how-to-find-best-hyperparameters-using-gridsearchcv-in-python/
* [3] https://www.projectpro.io/recipes/find-optimal-parameters-using-gridsearchcv
* [4] https://datagy.io/sklearn-gridsearchcv/
* [5] https://stats.stackexchange.com/questions/522771/can-the-value-obtained-from-gridsearchcv-be-used-to-find-the-best-model-after-hyper
* [6] https://www.programcreek.com/python/example/91151/sklearn.model_selection.GridSearchCV
* [7] https://www.projectpro.io/recipes/find-optimal-parameters-for-catboost-using-gridsearchcv-for-regression
* [8] https://datascience.stackexchange.com/questions/21877/how-to-use-the-output-of-gridsearch