Supervised Learning - Classification Week 7 Challenge
Answer No 1:-
PROS:
1- Thrives in High Dimension
When data has a high dimension (think 1000+ to infinity features) a Support Vector Machine with the right settings (right kernel choice etc.) can be the way to go and produce really accurate results.
2- Kernel Flexibility
If you're a hands-on person who likes to learn and understand the intricacies of systems then you might actually enjoy the highly integrated kernel world of Support Vector Machines.
Support Vector Machines are all about choosing the right kernel with the right parameters and this can provide lots of flexibility
and a potent toolset.
Linear kernels, non-linear kernels, polynomial kernels, RBF, sigmoid and Gaussian kernels all have an edge in solving supervised machine learning problems with SVMs or SVRs.
3- Fast Prediction
Support Vector Machines may be relatively sluggish when it comes to training, especially with large datasets, however, when it comes to prediction they are quite fast.
SVMs handle datasets as a whole at once and in this sense, it's not an incremental approach. This means the whole data is taken to the RAM of a computer during training. Once it's done prediction becomes a breeze.
4- Both Classification and Regression Skills
SVMs can be used to both classify data and also predict continuous numerical values. Regression variance of Support Vector Machines is usually called SVR (Support Vector Regression)
1- Advanced Settings
Although random forests have numerous optimization parameters too it's not so easy to make huge mistakes with them, but when it comes to Support Vector Machines, correct parameters can define the line between misery and victory.
This makes Support Vector Machines difficult to implement sometimes.
2- Costly Computation
SVMs are not the most efficient algorithms and it can be quite costly computationally to train them. (When applied with kernels and especially with non-linear kernels)
3- Suitable for Small Dataset
Support Vector Machines don't have a scalable nature and they don't work that well with mid-size or large datasets.
4- Feature Vectors Required
You can't just work on any problems with SVM Machine Learning Algorithms.
The dataset in hand will already need to have feature vectors or you will need to pre-process to extract feature vectors which might not always be easy or possible.
Overfitting is another potential side effect of Support Vector Machines and it can be quite
Answer No 2 :-Week 7 ml