Evaluating SVM Models

Evaluating the performance of SVM models is crucial for understanding how well they generalize to new data. In this recipe, we will explore key metrics for evaluating SVM models, including accuracy, precision, recall, and Receiver Operating Characteristic (ROC) curves. By applying these metrics, we can assess the strengths and weaknesses of our SVM models and make informed decisions about their deployment.

Getting ready

Before evaluating SVM models, 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
   from sklearn.svm import SVC
   from sklearn.datasets import load_breast_cancer
   from sklearn.metrics import classification_report, roc_curve, auc
   import pandas as pd
   import matplotlib.pyplot as plt

2. Load the dataset:

   data = load_breast_cancer()
   X = data.data
   y = data.target
   feature_names = data.feature_names
   df = pd.DataFrame(X, columns=feature_names)
   df[&apos...