Supervised Learning - Classification Week 7 Challenge

1.Pros and cons of SVM
->Advantages:
1.It has a high level of accuracy.

2.It works very weell with limited datasets.

3.Kernel SVM contains a non-linear transformation function to convert the complicated non-linearly separable data into linearly separable data

4.It is effective on datasets that have multiple features.

5.It is effective when the number of features are greater than the number of data points.

->Disadvatnges:

1.Does not work well with larger datasets.

2.Sometimes training time with svms can be high

3.It work best on small sample sets due to its high training time.