Supervised Learning - Classification Week 7 Challenge

1.

pros and cons of SVM :-

Pros

1) Gives good results even if there is not enough information about the data. Also works well with unstructured data.

2) Solves complex problems with a convenient kernel solution function.

3) Relatively good scaling of high-dimensional data.

4) SVM is relatively memory efficient

Cons

1) It is difficult to choose the appropriate kernel solution function

2) Training time is longer when using large data sets.

3) It may be difficult to interpret and understand because of problems caused by personal factors and the weighs of variables

4) The weights of the variables are not constant, thus the contribution of each variable to the output is variant