How to implement SVM in Python using Scikit-learn

Overview

Support Vector Machine (SVM) is a simple, supervised machine learning algorithm. SVMs are used for both classification and regression problems.

Note: You can learn more about SVMs here.

In this shot, we will implement an SVM classifier using the Scikit-learn toolkit.

We will use the digits dataset to train the SVM classifier model from scikit-learn. We split the data into train and test (70-30 split) to make sure the classification algorithm is able to generalize well to unseen data.

The model trained uses the learned parameters to classify into one of ten classes, that is, 0 to 9.

Code example

Explanation

• Line 3: We import the load_digits dataset from the sklearn library.
• Line 4: We import the train_test_split function from sklearn to split the data into train and test samples.
• Line 5: We import the SVC classifier from sklearn.
• Line 6: We use the sklearn provided confusion_matrix and accuracy_score functions.
• Line 9: We load the dataset into a local variable called dataset.
• Line 14: We split the data into test and train datasets. We use a 70-30 split, where 70% of the data is train and 30% is test. x-train and y_train contain the training data and labels respectively, while x_test and y_test contain the testing data and labels.
• Line 17: We define an SVM classifier called Classifier using a linear kernel.
• Line 20: We train the model on the training data and labels.
• Line 23: We use the trained parameters learned from the training data to predict the labels of the test data.
• Line 26: We use the accuracy_score function and predicted labels to find the accuracy of the model. We multiply by 100 to get the accuracy out of 100.
• Line 27: We use the predicted labels to find the confusion matrix.
• Line 30 to 32: We print the evaluation scores for the model.

Model performance

The SVM classifier we defined above gives a 98% accuracy on the digits dataset. The confusion matrix analysis shows that the model is performing really well.