[et_pb_section fb_built=”1″ _builder_version=”4.4.5″][et_pb_row _builder_version=”4.4.5″][et_pb_column type=”4_4″ _builder_version=”4.4.5″][et_pb_text _builder_version=”4.9.4″ header_font=”|700||on|||||”]<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.4.5″][et_pb_column type=”4_4″ _builder_version=”4.4.5″][et_pb_text _builder_version=”4.9.4″ header_2_font=”|||on|on||||”]<\/p>\n
This assignment is subject to change up until 3 weeks prior to the due date. We do not anticipate changes; any changes will be logged in this section.<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.4.5″][et_pb_column type=”4_4″ _builder_version=”4.4.5″][et_pb_text _builder_version=”4.9.4″ header_2_font=”|||on|on||||”]<\/p>\n In this assignment, you will implement four supervised learning machine learning algorithms from an algorithmic family called Classification and Regression Trees (CARTs).<\/span> You will also conduct several experiments to evaluate the behavior<\/span> and performance<\/span> of the learners as <\/span>you vary one of its hyperparameters<\/span>.<\/span> <\/span>You will submit the code for the project in <\/span>Gradescope<\/span> SUBMISSION. You will also submit <\/span>to <\/span>Canvas a <\/span>report <\/span>where you <\/span>discuss your experimental findings<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.4.5″][et_pb_column type=”4_4″ _builder_version=”4.4.5″][et_pb_text _builder_version=”4.9.4″ header_2_font=”|||on|on||||”]<\/p>\n I<\/span>mplement and evaluate four<\/span> CART<\/span> <\/span><\/span>regression<\/span><\/span> algorithms <\/span>in<\/span> <\/span>object-oriented <\/span>Python<\/span>: a \u201cclassic\u201d Decision Tree learner, a Random Tree learner, a Bootstrap Aggregating learner<\/span> (<\/span>i.e<\/span>, a \u201cbag learner\u201d)<\/span>, and an Insane Learner.<\/span> <\/span>As regression learners, the goal for your learner is to return a continuous numerical result (not a discrete result).<\/span> <\/span>You will use techniques introduced in the course lectures. However, this project may require readings or additional research to ensure an understanding of supervised learning, linear regression, learner performance, <\/span>performance metrics, <\/span>and CARTs (i.e., decision trees).<\/span><\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.4.5″][et_pb_column type=”4_4″ _builder_version=”4.4.5″][et_pb_text _builder_version=”4.9.6″ header_2_font=”|||on|on||||” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n You will implement four CART learners as regression learners: DTLearner, RTLearner, BagLearner, and InsaneLearner. Each of the learners must implement this <\/span>API specification<\/span><\/a>, where LinRegLearner is replaced by DTLearner, RTLearner, BagLearner, or InsaneLearner, as necessary.<\/span> In addition, each learner\u2019s constructor will need to be revised to align with the instantiation examples provided below.<\/span><\/p>\n This project has two main components: First, you will write code for each of the learners and for the experiments required for the report. You must write your own code for this project. You are NOT allowed to use other people\u2019s code or packages to implement these learners. Second, you will produce a report that summarizes the observation and analysis of several experiments. The experiments, analysis, and report should leverage the experimental techniques introduced in Project 1.<\/span>\u00a0<\/span><\/p>\n For the task below, you will mainly be working with the Istanbul datafile. This file includes the returns of multiple worldwide indexes for several days in history. In this task, the overall objective is to predict what the return for the MSCI Emerging Markets (EM) index will be based on the other index returns. Y in this case is the last column to the right of the Istanbul.csv file while the X values are the remaining columns to the left (except the first column). As part of reading the data file, your code should handle any data cleaning that is required. This includes the dropping of header rows and date-time columns (i.g., the first column of data in the Istanbul file, which should be ignored). Note that the local test script does this automatically for you, but you will have to handle it yourself when working on your report.<\/span>\u00a0<\/span><\/p>\n When the grading script tests your code, it randomly selects 60% of the data to train on and uses the other 40% for testing.<\/span>\u00a0<\/span><\/p>\n The other files, besides Istanbul.csv are there as alternative sets for you to test your code on. Each data file contains N+1 columns: X1, X2, \u2026 XN, and Y.<\/span>\u00a0<\/span><\/p>\n The Istanbul data is also available here: File:<\/span>Istanbul.csv<\/span><\/a>\u00a0<\/span><\/p>\n You will implement the following files:\u00a0<\/span>\u00a0<\/span><\/p>\n All your code will be placed into one of the four files (above). No other code files will be accepted. These files must remain and run from within the assess_learners directory. The testlearner.py file used to conduct your experiments is run using the following command:\u00a0<\/span><\/p>\n All of your code must be placed into one of the above files. No other code files will be accepted. The four learner files (i.e., DTLearner.py, RTLearner.py, BagLearner.py, InsaneLearner.py) must reside in the assess_learners folder. The testlearner.py file that is used to conduct your experiments must also reside in the assess_learners folder and is run using the following command:<\/p>\n [\/et_pb_text][et_pb_dmb_code_snippet code=”UFlUSE9OUEFUSD0uLi86LiBweXRob24gdGVzdGxlYXJuZXIucHkgRGF0YS9Jc3RhbmJ1bC5jc3Yg” _builder_version=”4.9.4″ _module_preset=”default”]UFlUSE9OUEFUSD0uLi86LiBweXRob24gdGVzdGxlYXJuZXIucHkgRGF0YS9Jc3RhbmJ1bC5jc3Yg[\/et_pb_dmb_code_snippet][et_pb_text _builder_version=”4.9.4″ _module_preset=”default”]<\/p>\n Implement a Decision Tree learner class named DTLearner in the file DTLearner.py. For this part of the project, your code should build a single tree only (not a forest). You should follow the algorithm outlined in the presentation here: <\/span>decision tree slides<\/span><\/a>.<\/span>\u00a0<\/span><\/p>\n\n
Overview<\/h2>\n
About The Project<\/h2>\n
Your Implementation<\/span><\/span>\u00a0<\/span><\/span><\/h2>\n
Task & Requirements<\/span><\/b>\u00a0<\/span><\/h3>\n
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Implement the DT and RT Learners (15 points each)<\/span><\/b>\u00a0<\/span><\/h3>\n