What is a good number of support vectors?

The minimum number of support vectors is two for your scenario. You don’t need more than two here. All of the support vectors lie exactly on the margin. Regardless of the number of dimensions or size of data set, the number of support vectors could be as little as 2.

Is it better to have more or less support vectors?

Therefore the computational complexity of the model is linear in the number of support vectors. Fewer support vectors means faster classification of test points. Both number of samples and number of attributes may influence the number of support vectors, making model more complex.

How many of the data points are support vectors?

5 points
Figure 15.1: The support vectors are the 5 points right up against the margin of the classifier.

What is the minimum number of support vectors that there can be for a data set which contains instances of each class )?

two support
A minimum of two support vectors are required for each decision hyperplane in the model. This follows from the observation that the margin at each decision boundary must be defined on each side of the dividing hyperplane by the closest data points, which are the support vectors.

Can support vectors lie outside the margin?

Almost every support vectors lie exactly on the margin. Support vectors are independent of the number of dimensions or size of the data set, the number of support vectors can be at least two. This technique is used when data is non- linearly separable. It is not required that our data points lie outside the margin.

Can SVM Overfit?

A standard SVM would try to separate blue and red classes by using the black curve line as a decision boundary. However, this is a too specific classification and highly likely to end up overfitting. An overfit SVM achieves a high accuracy with training set but will not perform well on new, previously unseen examples.

Can SVM overfit?

Why is SVM not prone to overfitting?

In SVM, to avoid overfitting, we choose a Soft Margin, instead of a Hard one i.e. we let some data points enter our margin intentionally (but we still penalize it) so that our classifier don’t overfit on our training sample. The higher the gamma, the higher the hyperplane tries to match the training data.

Is SVM sensitive to noise?

I have tranning set composed of 36 features. when I calculated “explained” value of PCA using Matlab. I notice that only the first 24 components are important.

Can support vector lie on the margin?

No, because the data points “on” the margin don’t contribute (they have zero weights) to the distance between the margin and the class specific data point close to the margin, which eventually ends up dictating the final selected class separation line.

Where do support vectors lie?

In the case of linearly separable data, the support vectors are those data points that lie (exactly) on the borders of the margins. These are the only points that are necessary to compute the margin (through the bias term b).