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GridSearchCV Hyper Parameter Tunning Machine Learning with Python
Machine Learning

How to do GridSearchCV Hyperparameter Tuning with Python

GridSearchCV Hyperparameter Tuning Machine Learning with Python

An exhaustive search over a given hyperparameter grid can be achieved using the popular GridSearchCV method from the scikit-learn library for Python hyper parametertuning. Hyperparameter tuning, which seeks to determine the optimal set of hyperparameters for a given model, is an important stage in the machine learning process.

Here’s how to use GridSearchCV to adjust hyperparameters:

First: Include the required libraries

import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
# to load the iris dataset 
from sklearn.datasets import load_iris
from sklearn.svm import SVC

Second: Prepare or load your dataset

In this example, the Iris dataset will be utilized.

# Load the Iris dataset
data = load_iris()
X, y =,

Third: From the data, create training and test sets

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Then: Build the model

# Create a support vector machine (SVM) classifier
svm_classifier = SVC()

Moving On: Describe the grid of hyperparameters

# Define the hyperparameter grid to search
param_grid = {
    'C': [0.1, 1, 10],
    'kernel': ['linear', 'rbf', 'poly'],
    'gamma': ['scale', 'auto']

Several values are being considered in this example for the regularization parameter (C), kernel (type of kernel function), and kernel coefficient (gamma) hyperparameters of the SVM classifier.

Subsequently: Run GridSearchCV to

# Create a GridSearchCV object with the SVM classifier and the hyperparameter grid
grid_search = GridSearchCV(svm_classifier, param_grid, cv=5, n_jobs=-1)

# Perform the grid search on the training data, y_train)

Finally: View the results here

Following GridSearchCV, you can view various properties to find out more about the ideal model and hyperparameters:

# Get the best hyperparameters found by GridSearchCV
best_params = grid_search.best_params_
print("Best Hyperparameters:", best_params)

# Get the best model with the best hyperparameters
best_model = grid_search.best_estimator_

Choose the best model by:

Lastly, you can use the best model that GridSearchCV finds to generate predictions, and you can evaluate the model’s efficacy using the test set:

# Use the best model to make predictions on the test data
y_pred = best_model.predict(X_test)

# Evaluate the model's performance
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")

Hence, GridSearchCV uses cross-validation to evaluate the model’s performance while conducting an extensive search across the hyperparameter grid (this is controlled by the cv parameter). As a scoring measure, it will return the set of hyperparameters that perform the best when using the classifier’s default mean accuracy.


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