One-Class Support Vector Machine is an unsupervised model for anomaly or outlier detection. Unlike the regular supervised SVM, the one-class SVM does not have target labels for the model training process. Instead, it learns the boundary for the normal data points and identifies the data outside the border to be anomalies.
In this post, we will use Python's sklearn library to implement one-class SVM. You will learn the following after reading the post
In this tutorial, we will go over the following topics:
👉 How to train a one-class support vector machine (SVM) model?
👉 How to predict anomalies from a one-class SVM model?
👉 How to change the default threshold for anomaly prediction?
👉 How to visualize the prediction results?
⏰ Timecodes ⏰
0:00 - Intro
0:50 - Step 1: Import Libraries
1:19 - Step 2: Create Imbalanced Dataset
1:48 - Step 3: Train Test Split
2:25 - Step 4: Train One-Class Support Vector Machine (SVM) Model
3:10 - Step 5: Predict Anomalies
3:44 - Step 6: Customize Predictions Using Scores
4:05 - Step 7: Visualization
4:28 - Summary
❤️ Blog post with code for this video:
https://grabngoinfo.com/one-class-support-vector-machine-svm-for-anomaly-detection/
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