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Decision Tree, Bagging Trees and Random Forests Implementation from scratch in Python.

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Decision Tree, Bagging Trees and Random Forests Implementation from scratch in Python.

Execute the following scripts to see the results:

python preprocess-assg4.py to preprocess and split the data into training and test set.

python trees.py trainingSet.csv testSet.csv 1 to run decision tree model.

python trees.py trainingSet.csv testSet.csv 2 to run bagging tree model.

python trees.py trainingSet.csv testSet.csv 1 to run random forest model with sampled sqrt(p) attributes.

python cv_depth.py to check the performance of models with varying depth limit of the trees.

python cv_frac.py to plot the learning curves of the three models.

python cv_numtrees.py to compare the performance of models with varying number of trees in the ensemble.

python neural_net.py trainingSet.csv testSet.csv to check the performance of dataset with neural networks.

Results for the above models and scripts are available in hw4.pdf

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