Support Vector Machine
Feature: supervised ; Common use: classification ; Output: class label ; Key Parameters: C(regularization), kernel, gamma
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
The advantages of support vector machines are:
Effective in high dimensional spaces.
Still effective in cases where number of dimensions is greater than the number of samples.
Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.
The disadvantages of support vector machines include:
If the number of features is much greater than the number of samples, avoid over-fitting in choosing Kernel functions and regularization term is crucial.
SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation (see Scores and probabilities, below).
What does SVM do?
SVM finds the best boundary (hyperplane) that separates classes with the maximum margin.
For 2D data, this boundary is a line.
For 3D+, it's a hyperplane.
The support vectors are the data points closest to this boundary and most critical in defining it.
Key Concepts
Hyperplane
Decision boundary that separates the classes
Margin
Distance between the hyperplane and the closest support vectors
Support Vectors
Data points closest to the hyperplane that influence its position
Kernel Trick
Technique that transforms data into a higher dimension to make it separable when it's not in original space
Types of Kernels:
Used to handle non-linear problems:
Linear: Default, used when data is linearly separable.
Polynomial: Useful for curved boundaries.
RBF (Radial Basis Function) / Gaussian: Most common for non-linear data.
SVC (Support Vector Classification)
SVC is the classification implementation of Support Vector Machine (SVM) in Scikit-learn, the popular Python machine learning library.
Usage
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load sample data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create SVC model
model = SVC(kernel='linear') # other options: 'rbf', 'poly'
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
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