Description
Objectives
1. Apply various classification algorithms to the movie reviews dataset
2. Use k-fold cross validation to identify the parameters that optimize performance
(generalization) for each method
3. Compare the accuracy and explainability of each method
Problem #1
For this homework, you will apply the following classification methods to the movie reviews
classification data (available in Blackboard)
1. Multinomial Naïve Bayes
2. Random Forest
3. Gradient Boosted Regression Trees
• Apply 4-fold cross-validation to the provided training data subset to train your classifiers and
identify their optimal parameters.
• After fixing the classifiers’ parameters, apply each method to the provided testing data subset to
predict and analyze your results. Compare the accuracy obtained during training (average of the
cross-validation folds) to those of the test data and comment on the results (overfitting,
underfitting, etc.)
• Analyze the results of each method by inspecting the feature importance (if applicable) and few
misclassified samples.
• Select the best algorithm and justify your choice based on accuracy, explainability, time required
to train/test, etc.
What to submit?
• A report that
o Describes your experiments,
o Summarizes, explains (using concepts covered in lectures) and compares the
results (using plots, tables, figures)
o Identifies the best method for each dataset.
• Do not submit your source code
• Do not submit raw output generated by your code!
• Your report needs to be a single file (MS Word or PDF)
• Your report cannot exceed 10 pages using a font of 12
• Assign numbers to all your figures/tables/plots and use these numbers to reference them in
your discussion
