Supervised Learning - Classification Week 7 Challenge

Q1. Pros and cons of SVM?

Ans:- Pros of SVM

1. SVM works relatively well when there is a clear margin of sepration between classes.

2.More effective in high dimensional spaces

3. It is effective in cases where the number of dimensions is greater than the number of samples

4. SVM is relatively memory efficient

----Cons of SVM

1. SVM algorithm is not suitable for large data sets.

2. It does not perform very well when the data set has more noise i.e. target classes are overlaping

3.In cases where the number of features for each data point exceeds the number of training data samples, the SVM will underperform.

4. As the support vector classifier works by putting data points, above and below the classifying hyperplane there is no probabilistic explanation for the classification.

Q2. Apply SVM to the “Surface defects in stainless steel plates” dataset and evaluate it??

Ans---

Test-train at 90 - 10 %

Test - train at 80 -20 %