Tuning SVM Parameters

As we’ve seen with all other ML models up to this point, hyperparameter tuning and optimization is a key step in improving the performance of SVMs as well. By adjusting parameters such as the regularization parameter (C) and kernel parameters, we can significantly improve the accuracy and robustness of SVM models. In this recipe, we will discover how to use classic grid search and cross-validation techniques to optimize SVM models using scikit-learn.

Getting ready

Before tuning SVM parameters, let’s ensure we have the necessary Python libraries installed and the dataset loaded:

1. Load the libraries:

   from sklearn.model_selection import train_test_split, GridSearchCV
   from sklearn.svm import SVC
   from sklearn.datasets import load_iris
   from sklearn.metrics import accuracy_score, classification_report
   import pandas as pd

2. Load the dataset:

   iris = load_iris()
   X = iris.data
   y = iris.target
   feature_names = iris.feature_names
   df = pd.DataFrame(X, columns=feature_names...