SVM is a supervised machine learning algorithm that can be used for classification or regression problems, but is mostly used for classification. It is effective in high dimensional spaces and cases where there are more dimensions than samples. SVMs use support vectors to make decisions, making them memory efficient. However, choosing the right kernel function and regularization is important to avoid overfitting when there are many more features than samples. SVMs also do not directly provide probability estimates.

1. SVM Classifier

2. Definition
“SupportVector Machine” (SVM) is a
supervised machine learning algorithm which
can be used for both classification or regression
challenges. However, it is mostly
used in classification problems.

3. Advantages
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.
.
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4. disadvantages
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

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