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Assignment 2

Due: Tuesday, October 13

For this assignment you will experiment with various classification models using subsets of some real-world data sets. In particular, you will use the K-Nearest-Neighbor algorithm to classify text documents, experiment with and compare classifiers that are part of the scikit-learn machine learning package for Python, and use some additional preprocessing capabilities of pandas and scikit-learn packages.

Please also see the video: Notes on Assignment 2 (55 mins) and the corresponding PPT slides: Notes on Assignment 2.

  1. K-Nearest-Neighbor (KNN) classification on Newsgroups [Dataset: newsgroups.zip]

For this problem you will use a subset of the 20 Newsgroup data set. The full data set contains 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups and has been often used for experiments in text applications of machine learning techniques, such as text classification and text clustering (see the description of the full dataset). The assignment data set contains a subset of 1000 documents and a vocabulary of  5,500 terms. Each document belongs to one of two classes Hockey (class label 1) and Microsoft Windows (class label 0). The data has already been split (80%, 20%) into training and test data. The class labels for the training and test data are also provided in separate files. The training and test data are on term x document format, containing a row for each term in the vocabulary and a column for each document. The values in the table represent raw term occurence counts. The data has already been preprocessed to extract tokens, remove stop words and perform stemming (so, the terms in the vocabulary are stems not full terms). Please be sure to read the readme.txt file in the distribution.

Your tasks in this problem are the following [Note: for this problem you should not use scikit-learn for classification, but create your own KNN classifer. You may use Pandas, NumPy, standard Python libraries, and Matplotlib.]

  1. Create your own KNN classifier function. Your classifier should allow as input the training data matrix, the training labels, the instance to be classified, the value of K, and should return the predicted class for the instance and the indices of the top K neighbors. Your classifier should work with Euclidean distance as well as Cosine distance (which is 1 minus the Cosine similarity). You may create two separate classifiers, or add the distance metric as a parameter in the classifier function (an example implementation of a KNN classifier was provided in class examples).
  2. Create an evaluation function to measure the accuracy of your classifier. This function will call the classifier function in part a on all the test instances and in each case compares the actual test class label to the predicted class label. It should take as input the training data, the training labels, the test instances, the labels for test instances, and the value of K. Your evaluation function should return the Classification Accuracy (ratio of correct predictions to the number of test instances) [See class notes: Classification & Prediction - Review of Basic Concepts].
  3. Run your evaluation function on a range of values for K from 5 to 100 (in increments of 5) in order to compare accuracy values for different numbers of neighbors. Do this both using Euclidean Distance as well as Cosine similarity measure. Present the results as graphs with K in the x-axis and the evaluation metric (accuracy) on the y-axis. Use a single plot to compare the two version of the classifier (Eculidean distance version vs. cosine similarity version).
  4. Next, modify the training and test data sets so that term weights are converted to TFxIDF weights (instead of raw term frequencies). [See class notes on Text Categorization]. Then, rerun your evaluation (only for the Cosine similairty version of the classifier) on the range of K values (as above) and compare the results to the results without using TFxIDF weights.
  5. Create a new classifier based on the Rocchio Method adapted for text categorization [See class notes on Text Categorization]. You should separate the training function from the classifiation function. The training part for the classifier can be implemented as a function that takes as input the training data matrix and the training labels, returning the prototype vectors for each class. The classification part can be implemented as another function that would take as input the prototypes returned from the training function and the instance to be classified. This function should measure Cosine similarity of the test instance to each prototype vector. Your output should indicate the predicted class for the test instance and the similarity values of the instance to each of the category prototypes.  Finally, use your evaluation function to compare your results to the best KNN results you obtained in part d. [Note: your functions should work regardless of the number of categories (class labels) and should not be limited to two-class categorization scenario.]

  1. Classification using scikit-learn [Dataset: bank_data.csv]

For this problem you will experiment with various classifiers provided as part of the scikit-learn (sklearn) machine learning module, as well as with some of its preprocessing and model evaluation capabilities.  [Note: This module is already part of the Anaconda distributions. You will work with a modified subset of a real data set of customers for a bank. This is the same data set used in Assignment 1. The data is provided in a CSV formatted file with the first row containing the attribute names. The description of the the different fields in the data are provided in this document.

Your tasks in this problem are the following:

  1. Load and preprocess the data using Pandas or similar tools. Specifically, you need to separate the target attribute ("pep") from the portion of the data to be used for training and testing. You will need to convert the selected dataset into the Standard Spreadsheet format (i.e., convert categorical attributes into numeric by creating dummy variables). Finally, split the transformed data into training and test sets (using 80%-20% randomized split). [Review Jupyter Notebooks from class to see examples of how to perform these tasks.]
  2. Run scikit-learn's KNN classifier on the test set. Note: in the case of KNN, you should first normalize the data so that all attributes are in the same scale (normalize so that the values are between 0 and 1). Generate the confusion matrix (visualize it using Matplotlib), as well as the classification report. Experiment with different values of K and the weight parameter (i.e., with or without distance weighting) for KNN to see if you can improve accuracy (you do not need to provide the details of all of your experimentation, but provide a short discussion on what parameters worked best as well as your final results).
  3. Using the non-normalized training and test data, perform classification using scikit-learn's decision tree classifier (using the default parameters). As above, generate the confusion matrix, classification report, and average accuracy scores the classifier. Compare the average accuracy score on the test and the training data sets. What does the comparison tell you in terms of bias-variance trade-off?
  4. Create another decision tree model (trained on the non-normalized training data) using "entropy" as the selection criteria, min_samples_split=10, and max_depth=5. For this model generate a visualization of tree and submit it as a separate file (png, jpg, or pdf) or embed it in the Jupyter Notebook.

  1. Data Analysis and Predictive Modeling on Census data [Dataset: adult-modified.csv]

For this problem you will use a simplified version of the Adult Census Data Set. In the subset provided here, some of the attributes have been removed and some preprocessing has been performed.

Your tasks in this problem are the following:

  1. Preprocessing and data analysis:
    • Examine the data for missing values. In case of categorical attributes, remove instances with missing values. In the case of numeric attributes, impute and fill-in the missing values using the attribute mean.
    • Examine the characteristics of the attributes, including summary statistics for the attributes, histograms illustrating the distribtions of numeric attributes, and bar graphs showing value counts for categorical attributes.
    • Perform the following cross-tabulations (including generating bar charts): education+race, work-class+income, work-class+race, and race+income. In the latter case (race+income) also create a table or chart showing percentages of each race category that fall in the low-income group. Discuss your observations from this analysis.
    • Compare and contrast the characteristics of the low-income and high-income categories across the different attributes.

  2. Predictive Modeling and Model Evaluation:
    • Using either Pandas or Scikit-learn, create dummy variables for the categorical attributes. Then separate the target attribute ("income_>50K") from the attributes used for training. [Note: you need to drop "income_<=50K" which is also created as a dummy variable in earlier steps). Split the data into training and test sets (80%-20% split).
    • Use scikit-learn to build classifiers usinng Naive Bayes (Gaussian), decision tree (using "gini" as selection criteria), and linear discriminant analysis (LDA).  For each of these perform 10-fold cross-validation on the training data (using cross-validation module in scikit-learn) and report the overall average accuracy. Compare this to the model accuracy on the training data. Finally, run your model on the set-aside test data.

Notes on Submission: You must submit your Jupyter Notebooks (similar to examples in class) which includes your documented code, results of your interactions, and any discussions or explanations of the results. Please organize your notebook so that it's clear what parts of the notebook correspond to which problems in the assignment. Please submit the notebook in both IPYNB and HTML formats (along with any auxiliary files). Your assignment should be submitted via D2L.

Copyright ©, Bamshad Mobasher, DePaul University.