Optimize RBF SVM Performance with Parameter Tuning

Introduction

This lab demonstrates how to tune the parameters of a Radial Basis Function (RBF) kernel SVM. The gamma and C parameters of the RBF kernel are crucial for the performance of the SVM model. The goal is to choose the optimal values of these parameters that maximize the accuracy of the model.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

%%%%{init: {'theme':'neutral'}}%%%% flowchart RL ml(("`Machine Learning`")) -.-> ml/FrameworkandSoftwareGroup(["`Framework and Software`"]) ml/FrameworkandSoftwareGroup -.-> ml/sklearn("`scikit-learn`") subgraph Lab Skills ml/sklearn -.-> lab-49258{{"`RBF SVM Parameter Tuning`"}} end

Load and Prepare Data Set

Load the iris dataset from scikit-learn.
Separate the data into feature matrix X and target vector y.
Standardize the feature matrix X using StandardScaler.
Create a simplified version of the dataset for decision function visualization by keeping only the first two features in X and sub-sampling the dataset to keep only two classes and make it a binary classification problem.

Train Classifiers

Create a logarithmic grid of the gamma and C parameters using np.logspace.
Split the data into training and testing sets using StratifiedShuffleSplit.
Perform a grid search using GridSearchCV to find the best parameters for the SVM model.
Fit a classifier for all parameters in the 2D version.

Visualization

Visualize the decision function for a variety of parameter values on a simplified classification problem involving only 2 input features and 2 possible target classes (binary classification).
Visualize the heatmap of the classifier's cross-validation accuracy as a function of C and gamma.

Interpretation

Interpret the results of the visualization and choose the optimal values for C and gamma.

Summary

This lab demonstrated how to tune the parameters of a Radial Basis Function (RBF) kernel SVM. The gamma and C parameters of the RBF kernel are crucial for the performance of the SVM model, and the optimal values for these parameters can be found using a combination of grid search and visualization techniques.

RBF SVM Parameter Tuning