Ai Svm — Free AI & ML Tutorial
Learn Ai Svm in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ai Svm in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Ai Svm in AI & ML
A Support Vector Machine (SVM) finds the best decision boundary (hyperplane) that separates two classes with the maximum margin. The "support vectors" are the data points closest to the boundary — they are the ones that define it.
Intuition: Imagine two groups of points on a piece of paper. Many lines can separate them, but SVM finds the line that is furthest from both groups simultaneously — maximising the safety margin. This makes SVM robust to new data.
The Kernel Trick: When data is not linearly separable (can't be divided by a straight line), SVM uses kernel functions to project data into a higher dimension where it becomes separable. Common kernels: Linear, RBF (Radial Basis Function), Polynomial.
SVMs work well with: high-dimensional data (text classification), small-to-medium datasets, and cases where you need a clear margin of separation. They are less ideal for very large datasets (slow training) or noisy data with heavy overlap.
Ai Svm — Syntax
# In scikit-learn: # from sklearn.svm import SVC # model = SVC(kernel='rbf', C=1.0, gamma='scale') # model.fit(X_train, y_train) # predictions = model.predict(X_test) # # C: regularisation — high C = fit training data tightly # kernel: 'linear', 'rbf', 'poly'
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