How to do this question in Machine learning past paper? Apart from the question,

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How to do this question in Machine learning past paper? Apart from the question, can anyone help to explain VC dimension in easiest way?

2. a) Show informally (using suitable diagrams) that a single layer perceptron has a VC dimension of at least 3. b) You are training a naive Bayes classifier. The available training data consists of instances with 3 binary features F1, F2, F3 and a binary valued label, L. i. What is naive about the naive Bayes classifier? ii. How many different parameters must be estimated in order to train the classifier? c) The following three-class confusion matrix represents the performance on test data of a classifier you are developing. i. What is the overall accuracy of the classifier? ii. What is the per-class precision of the classifier? iii. What is the weighted average precision of the classifier?

Explanation / Answer

1) Machine Learning:

Machine learning systems automatically learn programs from data. This is often a very attractive alternative to manually constructing them, and in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. Machine learning is used in Web search, spam filters, recommender systems, ad placement, credit scoring, fraud detection, stock trading, drug design, and many other applications

Explanation of VC dimension:

VC Dimension is a property of a set of functions { f (?) }

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