# Support Vector Machine

SVM is a supervised learning algorithm that classifies cases by finding a suitable separator(HYPERPLANE). As per its name suggests support vectors are responsible for its conduct. It pretends to be a discriminative classifier that is formally designed by a separative hyperplane. It is a representation of examples as a point in space that is mapped so that the points of different categories are separated by a gap which must be as wide as possible.

Support vectors are the nearest points on each side of the optimal hyperplane we have drawn. These support vectors are important to identify to draw the margin for the optimal hyperplane. Suppose our data is in a circular format, this can be solved with two faces:

By the use of best fit kernel out of Linear, Polynomial, RBF, and Sigmoid.

Map the data into higher dimensions.

Significance of the above points -

In order to find a suitable kernel, we need to implement all kernels and compare their accuracy to get a suitable kernel.

If the data(1D) is non-linearly separable than it can be converted into linearly separable by using a function such as f [x,x^2]. Mapping data into higher dimensional space.

__ADVANTAGES__

Accurate in higher dimensions.

Memory efficient.

__DISADVANTAGES__

Prone to overfitting.

Work on small datasets.

IMPLEMENTATIONS-

Bioinformatics

Face detection

Handwriting detection

Generalized prediction

Classification of images

Remote Homology detection

Text and Hypertext categorization

Here we perform it on breast cancer data, data is of classification type between begnin or malignant cancer. We have performed data cleaning to learn it visit __NORMALIZATION__.

Standardscalar( ) function is also explained in __NORMALIZATION__.

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