1 Use Xshape to get the number of features and the number of data points Report

# 1 use xshape to get the number of features and the

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1. Use X.shape to get the number of features and the number of data points. Report both numbers, mentioning which number is which. (5 points) 2. For each feature, plot a histogram ( plt.hist ) of the data values. (5 points) 3. Compute the mean & standard deviation of the data points for each feature ( np.mean , np.std ). (5 points) 4. For each pair of features (1,2), (1,3), and (1,4), plot a scatterplot (see plt.plot or plt.scatter ) of the feature values, colored according to their target value (class). (For example, plot all data points with y = 0 as blue, y = 1 as green, and y = 2 as red.) (5 points) 1 For example, by doing a Print Preview in Chrome and printing it to a PDF. Please also remember to check the resulting PDF before submitting. Homework 1 UC Irvine 1 / 4

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CS 178: Machine Learning & Data Mining Fall 2019 Problem 2: k-nearest-neighbor predictions (25 points) In this problem, you will continue to use the Iris data and create a k-nearest-neighbor (kNN) classifier using the provided knnClassify python class. While completing this problem, please explore the implementation to become familiar with how it works. First, we will shuffle and split the data into training and validation subsets: 1 iris = np . genfromtxt ( "data/iris.txt" , delimiter = None ) # load the data 2 Y = iris [:,-1] 3 X = iris [:,0:-1] 4 # Note: indexing with ":" indicates all values (in this case, all rows); 5 # indexing with a value ("0", "1", "-1", etc.) extracts only that value (here, columns); 6 # indexing rows/columns with a range ("1:-1") extracts any row/column in that range. 7 8 import mltools as ml 9 # We ' ll use some data manipulation routines in the provided class code 10 # Make sure the "mltools" directory is in a directory on your Python path, e.g., 11 # export PYTHONPATH=\$\\$\${PYTHONPATH}:/path/to/parent/dir 12 # or add it to your path inside Python: 13 # import sys 14 # sys.path.append( ' /path/to/parent/dir/ ' ); 15 16 np . random . seed (0) # set the random number seed 17 X , Y = ml . shuffleData ( X , Y ); # shuffle data randomly 18 # (This is a good idea in case your data are ordered in some systematic way.) 19 20 Xtr , Xva , Ytr , Yva = ml . splitData ( X , Y , 0.75); # split data into 75/25 train/validation Make sure to set the random number seed to 0 before calling shuffleData as in the example above (and in general, for every assignment). This ensures consistent behavior each time the code is run. Learner Objects Our learners (the parameterized functions that do the prediction) will be defined as python objects, derived from either an abstract classifier or abstract regressor class. The abstract base classes have a few useful functions, such as computing error rates or other measures of quality. More importantly, the learners will all follow a generic behavioral pattern, allowing us to train the function on one data set (i.e., set the parameters of the model to perform well on those data), and then make predictions on another data set.

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