Algorithm: Decision Trees

In my previous algorithm post, I talked about a family of algorithms called Naive Bayes.  These algorithms used Bayes' theorem, independence, and probabilities to determine whether a test case can be positively categorized.  However, these algorithms don't take into account the relationships between features.  Additionally, it would be nice to visualize how the model actually made decisions.  Fortunately, decision trees allow us to visualize the relationship of each property for classifying categories.

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Algorithm: Linear Regression

For anyone who starts out with machine learning, one of the first algorithms that one would learn is linear regression.

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