Support Vector Machine (SVM) is a powerful and versatile supervised machine learning algorithm used for both classification and regression. In this tutorial, we will delve into the basics of SVM, its working原理, and how to implement it in Python.
Key Concepts
- Supervised Learning: SVM is a supervised learning algorithm, which means it learns from labeled training data.
- Linear Separability: SVM tries to find the best hyperplane that separates the data into classes.
- Kernel Trick: SVM can handle non-linear data using the kernel trick.
How SVM Works
- Data Representation: SVM takes in input data in the form of feature vectors and their corresponding labels.
- Hyperplane: The goal of SVM is to find the best hyperplane that maximizes the margin between the two classes.
- Support Vectors: The data points closest to the hyperplane are called support vectors.
- Optimization: SVM solves an optimization problem to find the hyperplane that maximizes the margin.
Python Implementation
To implement SVM in Python, we can use the scikit-learn library.
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
# Load dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Create SVM classifier
clf = SVC(kernel='linear')
# Train the model
clf.fit(X_train, y_train)
# Predict
predictions = clf.predict(X_test)
# Evaluate
accuracy = clf.score(X_test, y_test)
print(f"Accuracy: {accuracy:.2f}")
Further Reading
To learn more about SVM and machine learning, check out the following resources:
SVM Hyperplane