I have 2 digits numbers and 9 features.
I must pick 2 features, so decided to plot the features against each other to see whether I can get any insight on the best features to train my algorithm.
The plot colours indicates the two digits.
Algorithms I considered to use were, K-Nearest Neighbour and Decision Tree. I am very new to machine learning, I chose these two algorithms simply because I have come across them.
Feature matrix of f1 to f9 against f1 to f9
Decision Tree decision boundary
I have a few questions:
Will choosing the feature x against feature y with the least amount of overlap will help achieve the optimal decision boundary?
When I look at features should I initially consider linear data separation. Then work my way up to use an algorithm that can deal with non-linear separated feature points?
What important visual properties should I look out for when choosing optimal features for training?
How can I visualise the tree in sklearn python?