** Why SVM performs better than other classifiers **

SVMs are different from other classification algorithms because of the way they choose the decision boundary that maximizes the distance from the nearest data points of all the classes. The decision boundary created by SVMs is called the maximum margin classifier or the maximum margin hyper plane.

** How SVM is better than other classifiers **

Advantages. SVM Classifiers offer good accuracy and perform faster prediction compared to Naïve Bayes algorithm. They also use less memory because they use a subset of training points in the decision phase. SVM works well with a clear margin of separation and with high dimensional space.

** Why is SVM good for classification **

Advantages of SVM Classifier:

SVM works relatively well when there is a clear margin of separation between classes. SVM is more effective in high dimensional spaces and is relatively memory efficient. SVM is effective in cases where the dimensions are greater than the number of samples.

** What is the advantage of SVM **

The advantages of SVM and support vector regression include that they can be used to avoid the difficulties of using linear functions in the high-dimensional feature space, and the optimization problem is transformed into dual convex quadratic programs.

** Why does SVM perform better than Naive Bayes **

Even though, NB gives good results when applied to short texts like tweets. For some datasets, NB may defeat other classifiers using feature selection. SVM is more powerful to address non-linear classification tasks. SVM generalizes well in high dimensional spaces like those corresponding to texts.

** Why does SVM give better accuracy **

It gives very good results in terms of accuracy when the data are linearly or non-linearly separable. When the data are linearly separable, the SVMs result is a separating hyperplane, which maximizes the margin of separation between classes, measured along a line perpendicular to the hyperplane.

** Why SVM is better than Naive Bayes **

Even though, NB gives good results when applied to short texts like tweets. For some datasets, NB may defeat other classifiers using feature selection. SVM is more powerful to address non-linear classification tasks. SVM generalizes well in high dimensional spaces like those corresponding to texts.

** Why SVM performs better than random forest **

SVM gives you distance to the boundary, you still need to convert it to probability somehow if you need probability. For those problems, where SVM applies, it generally performs better than Random Forest. SVM gives you "support vectors", that is points in each class closest to the boundary between classes.

** Why do we prefer SVM over neural networks **

We know that neural networks require significantly more time to train over a given dataset, with comparison to SVMs. Since, in this case, time is of the essence, our best bet is to use a support vector machine.

** Why is SVM more accurate than KNN **

KNN vs SVM :

SVM take cares of outliers better than KNN. If training data is much larger than no. of features(m>>n), KNN is better than SVM. SVM outperforms KNN when there are large features and lesser training data.