I've attended a few interviews for this very post and the few question that was asked in all of these interviews.
1. What are feature vectors?
A feature vector is an n-dimensional vector of numerical features that represent some object. In machine learning, feature vectors are used to represent numeric or symbolic characteristics, called features, of an object in a mathematical, easily analyzable way.
2. Steps in making decision tree
- take the entire data set as input
- analyze the split that maximizes the separation of classes
- Apply the split to the input data
- Re-apply steps 1 to 2 to the divided data.
- Stop when you meet some stopping criteria.
- This step is called pruning. Clean up the tree if you went too far doing splits.
Hope this helps!
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