By defauly, when using SVM models, the classification probabilties is not produced. This is different from a model like Logistic Regression which computes the classification of observations by the classficiation probability. However, it is sometimes nice to retrieve these probabilities and present them in a real life app. In this article, we will show you how to retrieve these probabilities from a SVM model.
To gather the probabilites from your SVM model, add the
probability param with the value of
# Load libraries from sklearn.svm import SVC from sklearn import datasets from sklearn.preprocessing import StandardScaler import numpy as np # Load data iris = datasets.load_iris() features = iris.data target = iris.target # Standardize features scaler = StandardScaler() features_standardized = scaler.fit_transform(features) # Create support vector classifier object svc = SVC(kernel="linear", probability=True, random_state=0) # Train classifier model = svc.fit(features_standardized, target) # Create new observation new_observation = [[.4, .4, .4, .4]] # View predicted probabilities model.predict_proba(new_observation)