Linear Regression

Linear Regression

  1. Load and preprocess the data using Pandas or Numpy and, if necessary, preprocessing functions from scikit-learn. The provided data is already normalized (see description), so there is no need for additional normalization. Compute and display basic statistics (mean, standard deviation, min, max, etc.) for each of the variables in the data set. Separate the target attribute for regression.
  2. Perform standard linear regression on data using the implementation for Ch. 8 of MLA. Compute the RMSE value on the full training data. Also, plot the correlation between the predicted and actual values of the target attribute. Display the obtained regression coefficients (weights). Finally, perform 10-fold cross-validation and compare the cross-validation RMSE to the training RMSE (for cross validation, you should use the KFold module from sklearn.cross_validation).
  3. Feature Selection:  use the scikit-learn regression model from sklearn.linear_model with a subset of features to perform linear regression. For feature selection, write a script or function that takes as input the training data, target variable; the model; and any other parameters you find necessary, and returns the optimal percentage of the most informative features to use. Your approach should use k-fold cross-validation on the training data (you can use k=5). You can use feature_selection.SelectPercentile to find the most informative variables. Show the list of most informative variables and their weights [Note: since this is regression not classification, you should use feature_selection.f_regression as scoring function rather than chi2). Next, plot the model’s mean absolute error values  on cross-validation relative to the percentage of selected features (See scikit-learn’s metrics.mean_absolute_error). In order to use cross_validation.cross_val_score with regression you’ll need to pass to it scoring=’mean_absolute_error’ as a parameter. [Hint: for an example of a similar feature selection process please review the class example notebook. Also, review scikit-learn documentation for feature selection.]
  4. Next, perform Ridge Regression and Lasso Regression using the modules from sklearn.linear_model. In each case, perform systematic model selection to identify the optimal alpha parameter. First, create a 20%-80% randomized split of the data. Set aside the test portion; the model selection process should be performed using the 80% training data partition. You should create a function that takes as input the data and target variable; the parameter to vary and a list of its values; the model to be trained; and any other relevant input needed to determine the optimal value for the specified parameter. The model selection process should perform k-fold cross validation (k should be a parameter, but you can select k=5 for this problem). You should also plot the error values on the training and cross-validation splits across the specified values of the alpha parameter. Finally, using the best alpha value, run the model on the set-aside test data. Discuss your observation and conclusions. [Hint: for an example of a similar model selection process please review the class example notebook.]
  5. Next, perform regression using Stochastic Gradient Descent for regression. For this part, you should use the SGDRegessor module from sklearn.linear_model. Again, start by a creating randomized 80%-20% train-test split. SGDRegessor requires that features be standardized (with 0 mean and scaled by standard deviation). Prior to fiting the model, perform the scaling using StandardScaler from sklearn.preprocessing. For this problem, perform a grid search (using GridSearchCV from sklearn.grid_search) Your grid search should compare combinations of two penalty parameters (‘l2’, ‘l1’) and different values of alpha (alpha could vary from 0.0001 which is the default to relatively large values, say 10). Using the best parameters, apply the model to the set-aside test data. Finally, perform model selection (similar to part d, above) to find the best “l1_ratio” parameter using SGDRegressor with  the “elasticnet” penalty parameter. [Note: “l1_ratio” is The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1;  l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1 penalty; defaults to 0.15.] Using the best mixing ratio, apply the Elastic Net model to the set-aside test data. Provide a summary of your findings from the above experiments.

Automatic Document Clustering

For this problem you will use a different subset of the20 Newsgroup data set that you used in Assignment 2  (see the description of the full dataset). The subset for this assignment includes 2,500 documents (newsgroup posts), each belonging to one of 5 categories windows (0), crypt (1), christian (2), hockey (3), forsale (4). The documents are represented by 9328 terms (stems). The dictionary (vocabulary) for the data set is given in the file “terms.txt” and the full term-by-document matrix is given in “matrix.txt” (comma separated values). The actual category labels for the documents are provided in the file “classes.txt”. Your goal in this assignment is to perform clustering on the documents and compare the clusters to the actual categories.

Your tasks in this problem are the following [Note: for the clustering part of this assignment you should use the kMeans module form Ch. 10 of MLA (use the version provided here as it includes some corrections to the book version). You may also use Pandas and other modules from scikit-learn that you may need for preprocessing or evaluation.]

  1. Create your own distance function that, instead of using Euclidean distance, uses Cosine similarity. This is the distance function you will use to pass to the kMeans function.
  2. Load the data set [Note: the data matrix provided has terms as rows and documents as columns. Since you will be clustering documents, you’ll need to take the transpose of this matrix so that your main data matrix is a document x term matrix. In Numpy, you may use the “.T” operation to obtain the transpose.] Then, split the data set (the document x term matrix) and set aside 20% for later use (see below). Use the 80% segment for clustering in the next part. The 20% portion must be a random subset.
  3. Perform Kmeans clustering on the training data. Write a function to display the top N terms in each cluster along with the cluster DF values for each term and the size of the cluster. The cluster DF value for a term t in a cluster C is the percentage of docs in cluster C in which term t appears (so, if a cluster has 500 documents, and term “game” appears in 100 of those 500 documents, then DF value of “game” in that cluster is 0.2 or 20%). Sort the terms for each cluster in decreasing order of the DF percentage. Here is an example of how this output might look like (here the top 10 terms for 3 of the 5 clusters are displayed in decreasing order of cluster DF values, but the mean frequency from the cluster centroid is also shown). [Extra Credit: use your favorite third party tool, ideally with a Python based API, to create a word cloud for each cluster.]
  4. Using the cluster assignments from Kmeans clustering, compare your 5 clusters to the 5 pre-assigned classes by computing the Completeness and Homogeneity values.
  5. Finally, using your cluster assignments as class labels, categorize each of the documents in the 20% set-aside data into each of the appropriate cluster. Your categorization should be based on Cosine similarity between each test document and cluster centroids. For each test document show the predicted class label as well as Cosine similarity to the corresponding cluster.

PCA for Reduced Dimensionality in Clustering

For this problem you will use an image segmentation data set for clustering. You will experiment with using PCA as an approach to reduce dimensionality and noise in the data. You will compare the results of clustering the data with and without PCA using the provided image class assignments as the ground truth. The data set is divided into three files. The file “segmentation_data.txt” contains data about images with each line corresponding to one image. Each image is represented by 19 features (these are the columns in the data and correspond to the feature names in the file “segmentation_names.txt”. The file “segmentation_classes.txt” contains the class labels (the type of image) and a numeric class label for each of the corresponding images in the data file. After clustering the image data, you will use the class labels to measure completeness and homogeneity of the generated clusters. The data set used in this problem is based on the Image Segmentation data set at the UCI Machine Learning Repository.

Your tasks in this problem are the following:

  1. Load in the image data matrix (with rows as images and columns as features). Also load in the numeric class labels from the segmentation class file. Using your favorite method (e.g., sklearn’s min-max scaler), perform min-max normalization on the data matrix so that each feature is scaled to [0,1] range.
  2. Next, Perform Kmeans clustering (for this problem, use the Kmeans implementation in scikit-learn) on the image data (since there are a total 7 pre-assigned image classes, you should use K = 7 in your clustering). Use Euclidean distance as your distance measure for the clustering. Print the cluster centroids (use some formatting so that they are visually understandable). Compare your 7 clusters to the 7 pre-assigned classes by computing the Completeness and Homogeneity values of the generated clusters.
  3. Perform PCA on the normalized image data matrix. You may use the linear algebra package in Numpy or the Decomposition module in scikit-learn (the latter is much more efficient). Analyze the principal components to determine the number, r, of PCs needed to capture at least 95% of variance in the data. Then use these r components as features to transform the data into a reduced dimension space. 
  4. Perform Kmeans again, but this time on the lower dimensional transformed data. Then, compute the Completeness and Homogeneity values of the new clusters.
  5. Discuss your observations based on the comparison of the two clustering results.

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