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What is SVM?

SVM finds the hyperplane that separates classes with the widest margin.

What is an SVM?

A support vector machine (SVM) chooses, among the boundaries separating two classes, the one farthest from the classes. The training points closest to that boundary are called support vectors; the boundary's position depends only on them.

from sklearn.svm import SVC
m = SVC(kernel="linear").fit(X, y)
print(m.support_vectors_.shape)   # support vector count x features
print(m.n_support_)               # per class

If the other points were deleted the model wouldn't change; that is why an SVM is memory-efficient and relatively robust to outliers.

Strengths of SVMs

SVMs are strong on medium-sized data (up to a few tens of thousands of examples) with many features: text classification, bioinformatics. With the kernel trick they learn non-linear boundaries.

  • Very scale-sensitive: StandardScaler first.
  • Training time grows fast with the number of examples; for very large data LinearSVC or SGDClassifier is preferred.
  • By default it produces a decision score, not a probability.

Practice Linear SVM

Train an SVC(kernel="linear") on separable two-cluster data. Print the number of support vectors (n_support_) and the predictions for [[0, 0], [4, 4]] on separate lines.

Starter code

model.py
import numpy as np
from sklearn.svm import SVC

X = np.array([[0, 0], [1, 0], [0, 1], [1, 1], [3, 3], [4, 3], [3, 4], [4, 4]])
y = np.array([0, 0, 0, 0, 1, 1, 1, 1])

# buraya yaz

Sample solution

model.py
import numpy as np
from sklearn.svm import SVC

X = np.array([[0, 0], [1, 0], [0, 1], [1, 1], [3, 3], [4, 3], [3, 4], [4, 4]])
y = np.array([0, 0, 0, 0, 1, 1, 1, 1])

m = SVC(kernel="linear").fit(X, y)
print(m.n_support_)
print(m.predict([[0, 0], [4, 4]]))

Expected output

[1 1]
[0 1]

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 Which points determine an SVM's boundary?

  • The class means
  • The support vectors Correct answer
  • All points equally
  • The points farthest from the boundary

Explanation: The points closest to the boundary define the margin; those far away have no effect.

2 Read the number of support vectors per class.

Question 
print(m.____)
Answer 
print(m.n_support_)

Explanation: n_support_ gives the number of support vectors per class.

3 Match the terms.

Term Meaning
support vector Point closest to the boundary
support_vectors_ Their coordinates
LinearSVC Fast for large data
StandardScaler Required before SVM

All lessons in Intermediate

1 scikit-learn basics

  1. What is scikit-learn?
  2. Estimator API
  3. The fit method
  4. The predict method
  5. The score method
  6. Summary - scikit-learn

2 Pipeline

  1. What is a Pipeline?
  2. Steps
  3. fit and predict
  4. Advantages
  5. ColumnTransformer
  6. Summary - Pipeline

3 Cross-validation

  1. What is cross-validation?
  2. K-fold
  3. cross_val_score
  4. Stratified
  5. Advantages
  6. Summary - Cross-validation

4 Grid search

  1. What is grid search?
  2. GridSearchCV
  3. param_grid
  4. best_params_
  5. best_score_
  6. Summary - Grid search

5 SVM

  1. What is SVM?
  2. Margin
  3. Kernel
  4. C parameter
  5. Classification
  6. Summary - SVM

6 Project: SVM

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

7 Random forest

  1. What is random forest?
  2. Ensemble
  3. n_estimators
  4. Bagging
  5. Feature importance
  6. Summary - Random forest

8 Boosting

  1. What is boosting?
  2. AdaBoost
  3. Gradient boosting
  4. Learning rate
  5. Advantages
  6. Summary - Boosting

9 Clustering

  1. What is clustering?
  2. KMeans
  3. Centroid
  4. Elbow method
  5. Labels
  6. Summary - Clustering

10 KMeans in depth

  1. KMeans algorithm
  2. Initialization
  3. n_init
  4. inertia_
  5. random_state
  6. Summary - KMeans

11 Hierarchical clustering

  1. What is hierarchical clustering?
  2. Dendrogram
  3. Agglomerative
  4. Linkage
  5. Advantages
  6. Summary - Hierarchical clustering

12 Project: Clustering

  1. Preparing data
  2. Training KMeans
  3. Label analysis
  4. Printing results

13 PCA

  1. What is PCA?
  2. Variance
  3. n_components
  4. fit_transform
  5. explained_variance_
  6. Summary - PCA

14 Dimensionality reduction

  1. What is dimensionality reduction?
  2. Why reduce?
  3. PCA vs t-SNE
  4. Visualization
  5. Advantages
  6. Summary - Dimensionality reduction

15 Feature selection

  1. What is feature selection?
  2. Variance threshold
  3. SelectKBest
  4. Recursive elimination
  5. Feature importance
  6. Summary - Feature selection

16 Imbalanced data

  1. What is imbalanced data?
  2. SMOTE
  3. Class weight
  4. Oversampling
  5. Metric selection
  6. Summary - Imbalanced data

17 Project: Features

  1. Feature extraction
  2. Feature selection
  3. Training model
  4. Evaluation

18 Ensemble methods

  1. What is ensemble?
  2. Voting
  3. Bagging
  4. Boosting
  5. Stacking
  6. Summary - Ensemble

19 Regularization

  1. What is regularization?
  2. L1 (Lasso)
  3. L2 (Ridge)
  4. alpha
  5. ElasticNet
  6. Summary - Regularization

20 Intermediate final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4