There is a balance between SVC maximizing the margin of the hyperplane and minimizing the misclassification. In SVC, the later is controlled with the hyperparameter \(C\), the penalty imposed on errors. C is a parameter of the SVC learner and is the penalty for misclassifying a data point. When C is small, the classifier is okay with misclassified data points (high bias but low variance). When C is large, the classifier is heavily penalized for misclassified data and therefore bends over backwards avoid any misclassified data points (low bias but high variance).
In scikit-learn, \(C\) is determined by the parameter
C and defaults to
C=1.0. We should treat \(C\) has a hyperparameter of our learning algorithm which we tune using model selection techniques.
# Load libraries from sklearn.svm import LinearSVC from sklearn import datasets from sklearn.preprocessing import StandardScaler import numpy as np
Load Iris Flower Data
# Load feature and target data iris = datasets.load_iris() X = iris.data y = iris.target
# Standarize features scaler = StandardScaler() X_std = scaler.fit_transform(X)
Train Support Vector Classifier
# Create support vector classifier svc = LinearSVC(C=1.0) # Train model model = svc.fit(X_std, y)
Create Previously Unseen Observation
# Create new observation new_observation = [[-0.7, 1.1, -1.1 , -1.7]]
Predict Class Of Observation
# Predict class of new observation svc.predict(new_observation)