SVM Hyperparameter Tuning using GridSearchCV - ML

Last Updated : 2 May, 2026

Support Vector Machines (SVM) are used for classification tasks but their performance depends on the right choice of hyperparameters like C and gamma. Finding the optimal combination of these hyperparameters can be an issue. GridSearchCV automates this process by systematically testing various combinations of hyperparameters and selecting the best one based on cross-validation results.

Let's see its implementation:

Step 1: Importing Necessary Libraries

We will be using Pandas, NumPy and Scikit-learn for building and evaluating the model.

Python 
import pandas as pd 
import numpy as np 
from sklearn.metrics import classification_report, confusion_matrix 
from sklearn.datasets import load_breast_cancer 
from sklearn.svm import SVC

Step 2: Loading and Printing the Dataset

In this example we will use Breast Cancer dataset from Scikit-learn. This dataset contains data about cell features and their corresponding cancer diagnosis i.e malignant or benign.

Python 
cancer = load_breast_cancer()

df_feat = pd.DataFrame(cancer['data'], columns=cancer['feature_names'])
df_target = pd.DataFrame(cancer['target'], columns=['Cancer'])

print("Feature Variables: ")
print(df_feat.info())
print("Dataframe looks like : ")
print(df_feat.head())

Output:

Step 3: Splitting the Data into Training and Testing Sets

We will split the dataset into training (70%) and testing (30%) sets using train_test_split. 

Python 
from sklearn.model_selection import train_test_split 

X_train, X_test, y_train, y_test = train_test_split( 
						df_feat, np.ravel(df_target), 
				test_size = 0.30, random_state = 101)

Step 4: Training an SVM Model without Hyperparameter Tuning

Before tuning the model let’s train a simple SVM classifier without any hyperparameter tuning.

Python 
model = SVC() 
model.fit(X_train, y_train) 

predictions = model.predict(X_test) 
print(classification_report(y_test, predictions))

Output:

svm3
Training without Hyperparameter Tuning

While the accuracy is around 92%, we can improve the model’s performance by tuning the hyperparameters.

Step 5: Hyperparameter Tuning with GridSearchCV

Now let’s use GridSearchCV to find the best combination of C, gamma and kernel hyperparameters for the SVM model. But before that let's understand these parameters:

  • C: Controls the trade-off between a wider margin (low C) and correctly classifying all points (high C).
  • gamma: Controls the influence of data points. High gamma leads to a tight boundary and may cause overfitting.
  • kernel: Defines the function used to transform data for separating classes. In this implementation, only the RBF kernel is used, which helps the SVM handle non-linear relationships in the data.
Python 
from sklearn.model_selection import GridSearchCV 

param_grid = {'C': [0.1, 1, 10, 100, 1000], 
			'gamma': [1, 0.1, 0.01, 0.001, 0.0001], 
			'kernel': ['rbf']} 

grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3) 
 
grid.fit(X_train, y_train)

Output:

svm4-
Hyperparameter Tuning with GridSearchCV

Step 6: Get the Best Hyperparameters and Model 

After grid search finishes we can check best hyperparameters and the optimized model.

Python 
print(grid.best_params_) 
 
print(grid.best_estimator_)

Output:

svm5
Hyperparameters and Model 

Step 7: Evaluating the Optimized Model

We can evaluate the optimized model on the test dataset.

Python 
grid_predictions = grid.predict(X_test) 

print(classification_report(y_test, grid_predictions))

Output:

svm6
Model evaluation

After hyperparameter tuning, the accuracy of the model increased to 94% showing that the tuning process improved the model’s performance. By using this approach, we can improve the model which helps in making it more accurate and reliable.

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