Spring 2020 Project 3: Assess Learners

Tasks

Implement DTLearner (15 points)

Implement a Decision Tree learner class named DTLearner in the file DTLearner.py. You should follow the algorithm outlined in the presentation here decision tree slides.

• We define "best feature to split on" as the feature (Xi) that has the highest absolute value correlation with Y.

The algorithm outlined in those slides is based on the paper by JR Quinlan which you may also want to review as a reference. Note that Quinlan's paper is focused on creating classification trees, while we're creating regression trees here, so you'll need to consider the differences.

For this part of the project, your code should build a single tree only (not a forest). We'll get to forests later in the project. Your code should support exactly the API defined below. DO NOT import any modules besides those listed in the allowed section below. You should implement the following functions/methods:

import DTLearner as dt
learner = dt.DTLearner(leaf_size = 1, verbose = False) # constructor
learner.addEvidence(Xtrain, Ytrain) # training step
Y = learner.query(Xtest) # query

Where "leaf_size" is the maximum number of samples to be aggregated at a leaf. While the tree is being constructed recursively, if there are leaf_size or fewer elements at the time of the recursive call, the data should be aggregated into a leaf. Xtrain and Xtest should be ndarrays (numpy objects) where each row represents an X1, X2, X3... XN set of feature values. The columns are the features and the rows are the individual example instances. Y and Ytrain are single dimension ndarrays that indicate the value we are attempting to predict with X.

If "verbose" is True, your code can print out information for debugging. If verbose = False your code should not generate ANY output. When we test your code, verbose will be False.

This code should not generate statistics or charts.

Implement RTLearner (15 points)

Implement a Random Tree learner class named RTLearner in the file RTLearner.py. This learner should behave exactly like your DTLearner, except that the choice of feature to split on should be made randomly. You should be able to accomplish this by removing a few lines from DTLearner (the ones that compute the correlation) and replacing the line that selects the feature with a call to a random number generator.

You should implement the following functions/methods:

import RTLearner as rt
learner = rt.RTLearner(leaf_size = 1, verbose = False) # constructor
learner.addEvidence(Xtrain, Ytrain) # training step
Y = learner.query(Xtest) # query