Decision Tree - Hyperparameter to Optimize - Statistic Assignment Help

Assignment Task:

Take any reasonable data set you like with at least 200 observations on which you would want build a decision tree via impurity reduction (like we did in class). If you want to force a particular column to be similar to the binary "star/galaxy" we did in class, that's fine. For example, if the column is Height and has values 6.0, 5.2, 3.1...you are free to replace that column with Height_greater_than_5: 1, 1, 0...where 1 means true and 0 means false.

Next, shuffle the data set and create three sets with it: a training set, a validation set, and a test set. You are free to use any appropriate split, but I will recommend 50% for training, 30% for validation, and 20% for test.

As we discussed, decision trees have many hyperparameters, none of which are obvious. One such hyperparameter is "a split must produce individual leaves containing at least X observations in order to be allowed". Another such hyperparameter is "a split must reduce impurity by Y in order to be allowed".

Choose 1 hyperparameter to optimize. Build decision trees with various settings of that hyperparameter, evaluating their accuracy with your validation data.

When you find the hyperparameter setting that maximizes validation accuracy, see how that optimal decision tree performs in simulated production (your test data).

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