a2_q2.ipynb

gistfile1.txt

Problem 2 (5 points)
Load dataset in problem2data.txt by using “numpy.loadtxt()” in Python, which contains two synthetic classes separated by a non-linear function y = f (x), with additive noise. The last column contains “0” or “1”, indicating the corresponding classes. You are asked to investigate the extent to which polynomial functions can be used to build a logistic regression classifier.
(a) Build a logistic regression classifier by using a polynomial function of order 1 (e.g., θ0 + θ1x1 + θ2x2). Apply 5-fold-cross-validation to generate training and test sets. Use the training set to train the model. Compute its five F1 scores on test set (one F1 score for each fold). Compute its five F1 scores on training set. You will need “f1 score” from “sklearn.metrics”.
(b) Repeat part (a) for polynomials of order 2 to 12. You will need “PolynomialFeatures” from “sklearn.preprocessing”.
(c) Repeatparts(a-b)50times,andestimatetheaverageF1scoreforeachpolynomialorder(average across its 5 × 50 runs, both for training and test sets). Generate a plot that shows the F1 scores versus the polynomial order.
(d) Discuss how the F1 scores of the model changes as a function of the polynomial order.
(e) Use Jupyter Notebook to perform implementation. You are required to submit your “.ipynb” file. You could use “LogisticRegression” from “sklearn.linear model”. Do not add any regulariza- tions.

problem2data.txt

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