Description
For Assignment 4, we will train and interpret model on wine- quality dataset. You can access the data from following link. There are two csv files available on the link, but you only need to work on white-wine dataset. Treat this dataset as a regression problem where 1 is poor and 10 is excellent quality. Use R-squared metrics for model evaluation.
https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/
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Train a Random Forest Regressor for the dataset. Find the best model based on R-squared value using RandomizedSearchCV. [10 Marks]
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Use the best model from question 1 for model interpretation and rank the features based on drop feature importance. [15 Marks]
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Use the best model from question 1 for model interpretation and rank the features based on permutation importance. [15 Marks]
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Use the best model from question 1 for model interpretation and rank the features based on SHAP algorithm. Install SHAP using pip. [20 Marks]
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Visualize partial dependence plot for each feature in the dataset using Sklearn. [10 Marks]
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Visualize ICE plot for each feature using following library. http://austinrochford.github.io/PyCEbox/
[20 Marks]
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Analyze outputs from each technique and comment that which technique you found most useful and why. [10 Marks]
Please save your notebook with all the images and comments before submitting.
