Support Vector Machine (SVM) is a powerful supervised learning algorithm used for both classification and regression tasks. In this tutorial, we will explore the basics of SVM, its working principle, and how to implement it using Python.

Key Concepts

  • Supervised Learning: SVM is a supervised learning algorithm that requires labeled training data.
  • Hyperplane: SVM finds the best hyperplane that separates the data into classes.
  • Support Vectors: These are the data points that lie closest to the hyperplane.

SVM in Python

To implement SVM in Python, we can use the scikit-learn library. Here's a simple example:

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import svm

# Load the dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create an SVM classifier
clf = svm.SVC(kernel='linear')

# Train the classifier
clf.fit(X_train, y_train)

# Predict the labels for the test set
y_pred = clf.predict(X_test)

# Evaluate the classifier
print("Accuracy:", clf.score(X_test, y_test))

Further Reading

For more information on SVM, you can refer to the following resources:

SVM Hyperplane