SVM

TL;DR

• A supervised algorithm used for classification and regression that finds a hyperplane which maximally separates classes.
• Works by representing examples as points in a high-dimensional feature space and training on labeled data.
• Commonly applied to problems like spam filtering, image classification, and predicting housing prices.

Definition

Support Vector Machines (SVMs) are a type of supervised machine learning algorithm that can be used for classification or regression tasks. The goal of an SVM is to find the hyperplane in a high-dimensional space that maximally separates the different classes.

Explanation

• Convert raw inputs into features (for example, counts or keyword presence) so each example is a point in a high-dimensional space.
• Train the SVM on a labeled dataset; the algorithm searches for the hyperplane that maximally separates examples of different classes.
• For classification, new examples are assigned a class based on which side of the hyperplane they fall on. For regression, the same principles are applied to predict continuous outcomes.
• SVMs are known for their ability to handle high-dimensional data, though practical problems can remain challenging due to data dimensionality and domain complexity.

Examples

Classifying Email as Spam or Not Spam

• Extract features from emails such as the number of words, the presence of certain keywords, and the sender’s email address.
• Represent each email as a point in a high-dimensional space.
• Train the SVM on a labeled dataset where each email is labeled as either spam or not spam.
• The trained SVM finds the hyperplane that maximally separates spam emails from non-spam emails.
• Classify new emails by seeing which side of the hyperplane they fall on.

Predicting Housing Prices

• Extract relevant features such as the size of the house, the number of bedrooms, the location, etc., and represent each house as a point in a high-dimensional space.
• Train the SVM on a labeled dataset where each house is labeled with its actual price.
• The SVM tries to find the hyperplane that maximally separates houses with high prices from those with low prices.
• Use the trained model to predict prices of new houses by determining which side of the hyperplane they fall on.
• Note: this task can be challenging in practice due to the high-dimensional nature of the data and the complexity of the real estate market.

Use cases

• Spam filtering
• Image classification
• Predicting housing prices

Notes or pitfalls

• High-dimensional data and domain complexity can make practical problems challenging.
• Despite challenges, SVMs are known for their ability to handle high-dimensional feature spaces.

Related terms

• Hyperplane
• Classification
• Regression
• Supervised machine learning