Homework answers / question archive / CSE 6363 - Machine Learning Fall 2022 Due Date: Nov 19, 2022, 11:59 PM Data Set Use the dataset given at the bottom of this file

CSE 6363 - Machine Learning Fall 2022 Due Date: Nov 19, 2022, 11:59 PM Data Set Use the dataset given at the bottom of this file

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CSE 6363 - Machine Learning
Fall 2022
Due Date: Nov 19, 2022, 11:59 PM
Data Set
Use the dataset given at the bottom of this file.
Do Not Use
You are not allowed to use any ML libraries other than NumPy.
You cannot use sklearn. If used, you will receive a penalty of 90 points.
You cannot use pandas. If used, you will receive a penalty of 20 points.
Libraries
You are allowed to use NumPy, math.
If you want to use any other library apart from these, please check with your GTA and get their approval.
Where to code
1. We will provide you with a directory structure with python files for each part of every question. You must write your code in these files.
2. It will contain a script to execute the files. You must run this script and verify that your code runs before you submit.
Decision Trees:
1. Consider the problem from the previous assignments where we want to predict gender from information about height, weight, and age. We will use Decision Trees to make this prediction. Note that as the data attributes are continuous numbers you have to use the ≥ attribute and determine a threshold for each node in the tree. As a result, you need to solve the information gain for each threshold that is halfway between two data points and thus the complexity of the computations increases with the number of data items.
a) Implement a decision tree learner for this particular problem that can derive decision trees with an arbitrary, pre-determined depth (up to the maximum depth where all data sets at the leaves are pure) using the information gain criterion.
b) Divide the data set from Question 1c) in Project 1 (the large training data set) into a training set comprising the first 50 data points and a test set consisting of the last 70 data elements. Use the resulting training set to derive trees of depths 1 - 5 and evaluate the accuracy of the resulting trees for the 50 training samples and for the test set containing the last 70 data items. Compare the classification accuracy on the test set with the one on the training set for each tree depth. For which depths does the result indicate overfitting?
Data for Questions (same as the data for Project 1)
Training Data
(( 1.5963600450124, 75.717194178189, 23), W ) (( 1.6990610819676, 83.477307503684, 25), M ) (( 1.5052092436, 74.642420817737, 21), W ) (( 1.5738635789008, 78.562465284603, 30), M ) (( 1.796178772769, 74.566117057707, 29), M ) (( 1.6274618774347, 82.250591567161, 21), W ) (( 1.6396843250708, 71.37567170848, 20), W ) (( 1.538505823668, 77.418902097029, 32), W ) (( 1.6488692005889, 76.333044488477, 26), W ) (( 1.7233804613095, 85.812112126306, 27), M ) (( 1.7389100516771, 76.424421782215, 24), W ) (( 1.5775696242624, 77.201404139171, 29), W ) (( 1.7359417237856, 77.004988515324, 20), M ) (( 1.5510482441354, 72.950756316157, 24), W ) (( 1.5765653263667, 74.750113664457, 34), W ) (( 1.4916026885377, 65.880438515643, 28), W ) (( 1.6755053770068, 78.901754249459, 22), M ) (( 1.4805881225567, 69.652364469244, 30), W ) (( 1.6343943760912, 73.998278712613, 30), W ) (( 1.6338449829543, 79.216500811112, 27), W ) (( 1.5014451222259, 66.917339299419, 27), W ) (( 1.8575887178701, 79.942454850988, 28), M ) (( 1.6805940669394, 78.213519314007, 27), W ) (( 1.6888905106948, 83.031099742808, 20), M ) (( 1.7055120272359, 84.233282531303, 18), M ) (( 1.5681965896812, 74.753880204215, 22), W ) (( 1.6857758389206, 84.014217544019, 25), W ) (( 1.7767370337678, 75.709336556562, 27), M ) (( 1.6760125952287, 74.034126149139, 28), M ) (( 1.5999112612548, 72.040030344184, 27), M ) (( 1.6770845322305, 76.149431872551, 25), M ) (( 1.7596128136991, 87.366395298795, 29), M ) (( 1.5344541456027, 73.832214971449, 22), W ) (( 1.5992629534387, 82.4806916967, 34), W ) (( 1.6714162787917, 67.986534194515, 29), W ) (( 1.7070831676329, 78.269583353177, 25), M ) (( 1.5691295338456, 81.09431696972, 27), M ) (( 1.7767893419281, 76.910413184648, 30), M ) (( 1.5448153215763, 76.888087599642, 32), W ) (( 1.5452842691008, 69.761889289463, 30), W ) (( 1.6469991919639, 82.289126983444, 18), W ) (( 1.6353732734723, 77.829257585654, 19), W ) (( 1.7175342426502, 85.002276406574, 26), M ) (( 1.6163551692382, 77.247935733799, 21), M ) (( 1.6876845881843, 85.616829192322, 27), M ) (( 1.5472705508274, 64.474350365634, 23), W ) (( 1.558229415357, 80.382011318379, 21), W ) (( 1.6242189230632, 69.567339939973, 28), W ) (( 1.8215645865237, 78.163631826626, 22), W ) (( 1.6984142478298, 69.884030497097, 26), M )
Testing Data
(( 1.6468551415123, 82.666468220128, 29), M ) (( 1.5727791290292, 75.545348033094, 24), M ) (( 1.8086593470477, 78.093913654921, 27), M ) (( 1.613966988578, 76.083586505149, 23), W ) (( 1.6603990297076, 70.539053122611, 24), M ) (( 1.6737443242383, 66.042005829182, 28), W ) (( 1.6824912337281, 81.061984274536, 29), M ) (( 1.5301691510101, 77.26547501308, 22), M ) (( 1.7392340943261, 92.752488433153, 24), M ) (( 1.6427105169884, 83.322790265985, 30), M ) (( 1.5889040551166, 74.848224733663, 25), W ) (( 1.5051718284868, 80.078271153645, 31), W ) (( 1.729420786579, 81.936423109142, 26), M ) (( 1.7352568354092, 85.497712687992, 19), M ) (( 1.5056950011245, 73.726557750383, 24), W ) (( 1.772404089054, 75.534265951718, 30), M ) (( 1.5212346939173, 74.355845722315, 29), W ) (( 1.8184515409355, 85.705767969326, 25), M ) (( 1.7307897479464, 84.277029918205, 28), W ) (( 1.6372690389158, 72.289040612489, 27), M ) (( 1.6856953072545, 70.406532419182, 28), W ) (( 1.832494802635, 81.627925524191, 27), M ) (( 1.5061197864796, 85.886760677468, 31), W ) (( 1.5970906671458, 71.755566818152, 27), W ) (( 1.6780459059283, 78.900587239209, 25), W ) (( 1.6356901170146, 84.066566323977, 21), W ) (( 1.6085494116591, 70.950456539016, 30), M ) (( 1.5873479102442, 77.558144903338, 25), M ) (( 1.7542078120838, 75.3117550236, 26), M ) (( 1.642417315747, 67.97377818999, 31), W ) (( 1.5744266340913, 81.767568318602, 23), M ) (( 1.8470601407979, 68.606183538532, 30), W ) (( 1.7119387468283, 80.560922353487, 27), W ) (( 1.6169930563306, 75.538611935125, 27), M ) (( 1.6355653058986, 78.49626023408, 24), M ) (( 1.6035395957618, 79.226052358485, 33), M ) (( 1.662787957279, 76.865925681154, 25), M ) (( 1.5889291137091, 76.548543553914, 28), W ) (( 1.9058127964477, 82.56539915922, 25), M ) (( 1.694633493614, 62.870480634419, 21), W ) (( 1.7635692396034, 82.479783004684, 27), M ) (( 1.6645292231449, 75.838104636904, 29), W ) (( 1.7201968406129, 81.134689293557, 24), W ) (( 1.5775563651749, 65.920103519266, 24), W ) (( 1.6521294216004, 83.312640709417, 28), M ) (( 1.5597501915973, 76.475667826389, 30), W ) (( 1.7847561120027, 83.363676219109, 29), M ) (( 1.6765690500715, 73.98959022721, 23), M ) (( 1.6749260607992, 73.687015573315, 27), W ) (( 1.58582362825, 71.713707691505, 28), M ) (( 1.5893375739649, 74.248033504548, 27), W ) (( 1.6084440045081, 71.126430164213, 27), W ) (( 1.6048804804343, 82.049319162211, 26), W ) (( 1.5774196609804, 70.878214496062, 24), W ) (( 1.6799586185525, 75.649534976838, 29), W ) (( 1.7315642636281, 92.12183674186, 29), M ) (( 1.5563282000349, 69.312673560451, 32), W ) (( 1.7784349641893, 83.464562543, 26), M )
(( 1.7270244609765, 76.599791001341, 22), W ) (( 1.6372540837311, 74.746741127229, 30), W ) (( 1.582550559056, 73.440027907722, 23), W ) (( 1.722864383186, 79.37821152354, 20), W ) (( 1.5247544081009, 70.601290492141, 27), W ) (( 1.580858666774, 70.146982323579, 24), W ) (( 1.703343390074, 90.153276095421, 22), W ) (( 1.5339948635367, 59.675627532338, 25), W ) (( 1.8095306490733, 86.001187990639, 20), M ) (( 1.7454786971676, 85.212429336602, 22), M ) (( 1.6343303342105, 85.46378358014, 32), M ) (( 1.5983479173071, 79.323905480504, 27), W )