[et_pb_section fb_built=”1″ admin_label=”Section” _builder_version=”4.10.4″ global_colors_info=”{}”][et_pb_row admin_label=”Project Title” _builder_version=”4.4.5″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.4.5″ global_colors_info=”{}”][et_pb_text _builder_version=”4.10.4″ header_font=”|700||on|||||” global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row use_custom_gutter=”on” gutter_width=”1″ admin_label=”row” _builder_version=”4.4.1″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” width=”100%” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.4.1″ global_colors_info=”{}”][et_pb_divider color=”#eeeeee” divider_position=”center” divider_weight=”3px” _builder_version=”4.4.1″ width=”25%” custom_padding=”30px||30px||true|false” global_colors_info=”{}”][\/et_pb_divider][et_pb_blurb title=”Table of Contents” use_icon=”on” font_icon=”h||divi||400″ icon_color=”rgba(0,0,0,0.05)” icon_placement=”left” image_icon_width=”100px” content_max_width=”100%” _builder_version=”4.13.0″ header_level=”h2″ header_font_size=”26px” height=”38px” icon_font_size=”100px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Overview” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.14.7″ header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#overview” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”"||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”About the Project” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.14.7″ header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#about” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”"||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”Your Implementation” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.14.7″ header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#implementation” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”"||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”Contents of Report” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.14.7″ header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#report” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”"||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”Testing Recommendations” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.14.7″ header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#testing” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”"||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”Submission Requirements” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.14.7″ header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#submission” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”"||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”Grading Information” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.14.7″ header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#grading” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”"||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”Development Guidelines” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.14.7″ header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#guidelines” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”"||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”Optional Resources” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.14.7″ header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#optional” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”"||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_divider color=”#eeeeee” divider_position=”center” divider_weight=”3px” _builder_version=”4.4.1″ width=”25%” custom_padding=”30px||30px||true|false” global_colors_info=”{}”][\/et_pb_divider][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=”Revisions” _builder_version=”4.4.5″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.4.5″ global_colors_info=”{}”][et_pb_text admin_label=”Text” _builder_version=”4.14.7″ hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/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 11 Feb 2022<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=”1 Overview” module_id=”overview” _builder_version=”4.14.7″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.4.5″ global_colors_info=”{}”][et_pb_text _builder_version=”4.10.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n In this assignment, you will implement four supervised learning machine learning algorithms from an algorithmic family called Classification and Regression Trees (CARTs). You will also conduct several experiments to evaluate the behavior and performance of the learners as you vary one of its hyperparameters. You will submit the code for the project in <\/span>Gradescope<\/span> SUBMISSION. You will also submit to Canvas a report where you discuss your experimental findings<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_text admin_label=”1.1 Learning Objectives” _builder_version=”4.10.4″ global_colors_info=”{}”]<\/p>\n The specific learning objectives for this assignment are focused on the following areas:<\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=”2 About The Project” module_id=”about” _builder_version=”4.14.7″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.4.5″ global_colors_info=”{}”][et_pb_text _builder_version=”4.10.4″ global_colors_info=”{}”]<\/p>\n Implement and evaluate four CART regression algorithms in object-oriented Python: a \u201cclassic\u201d Decision Tree learner, a Random Tree learner, a Bootstrap Aggregating learner (<\/span>i.e<\/span>, a \u201cbag learner\u201d), and an Insane Learner. As regression learners, the goal for your learner is to return a continuous numerical result (not a discrete result). 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, performance metrics, and CARTs (i.e., decision trees)<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=”3 Your Implementation” module_id=”implementation” _builder_version=”4.14.7″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.4.5″ global_colors_info=”{}”][et_pb_text admin_label=”Text” _builder_version=”4.14.4″ global_colors_info=”{}”]<\/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. In addition, each learner\u2019s constructor will need to be revised to align with the instantiation examples provided below.<\/span>\u00a0<\/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.\u00a0<\/span>\u00a0<\/span><\/p>\n For the task below, you will mainly be working with the Istanbul data file. 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 cleansing 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 <\/span>test script<\/span><\/i> does this automatically for you, but you will have to handle it yourself when developing your implementation.\u00a0<\/span>\u00a0<\/span><\/p>\n The Istanbul data is also available here: <\/span>Istanbul.csv<\/span><\/a>\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.\u00a0<\/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, (collectively called the <\/span>feature<\/span><\/i>s) and Y (referred to as the <\/span>target<\/span><\/i>).\u00a0<\/span>\u00a0<\/span><\/p>\n Before the deadline, make sure to pre-validate your submission using Gradescope TESTING. Once you are satisfied with the results in testing, submit the code to Gradescope SUBMISSION. <\/span>Only code submitted to Gradescope SUBMISSION will be graded. If you submit your code to Gradescope TESTING and have not also submitted your code to Gradescope SUBMISSION, you will receive a zero (0).<\/span><\/b>\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_text admin_label=”3.1 Getting Started” _builder_version=”4.14.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n To make it easier to get started on the project and focus on the concepts involved, you will be given a starter framework. This framework assumes you have already set up the <\/span><\/span>local environment<\/span><\/span><\/a> and <\/span><\/span>ML4T Software<\/span><\/span><\/a>. <\/span>The framework for Project <\/span>3<\/span> can be obtained from:\u202f<\/span><\/span>Assess_Learners_2022Spr.zip<\/a>.\u202f<\/span><\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/lucylabs.gatech.edu\/ml4t\/wp-content\/uploads\/2022\/01\/assess_learner_file_structure-300×241-1.png” title_text=”assess_learner_file_structure” admin_label=”directory structure image” _builder_version=”4.10.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text admin_label=”3.1 Getting Started – Cont” _builder_version=”4.10.7″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Extract its contents into the base directory (e.g., ML4T_2021Summer). This will add a new folder called \u201cassess_learners\u201d to the course directory structure:\u202f\u202f<\/span>\u00a0<\/span><\/p>\n The framework for Project 3 can be obtained in the assess_learners folder alone. Within the assess_learners folder are several files:\u00a0<\/span>\u00a0<\/span><\/p>\n The data files that your learners will use for this project are contained in the Data folder. (Note the distinction between the \u201c<\/span>d<\/strong>ata\u201d folder created as part of the local environment and the \u201c<\/span>D<\/span><\/b>ata\u201d folder within the assess_learners folder that will be used in this assignment.)\u00a0<\/span>\u00a0<\/span><\/p>\n LinRegLearner is available for your use and must not be modified. However, you can use it as a template for implementing your learner classes. The testlearner.py file contains a simple testing scaffold that you can use to test your learners, which is useful for debugging. It must also be modified to run the experiments. The grade_learners.py file is a local pre-validation script that mirrors the script used in the Gradescope TESTING environment.<\/span>\u00a0<\/span><\/p>\n You will need to create the learners using the following names: DTLearner.py, RTLearner.py, BagLearner.py, and InsaneLearner.py.<\/span>\u00a0<\/span><\/p>\n In the assess_learners\/Data directory you will find several datasets:\u00a0<\/span>\u00a0<\/span><\/p>\n In these files, we have provided test data for you to use in determining the correctness of the output of your learners. Each data file contains N+1 columns: X1, X2, \u2026 XN, and Y.<\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_text admin_label=”3.2 Task & Requirements” _builder_version=”4.10.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n You will implement the following files:\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n All your code must be placed into one of the above files. No other code files will be accepted. All files must reside in the assess_learners folder. The testlearner.py file that is used to conduct your experiments is run using the following command:<\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_code admin_label=”pythonpath testlearner code” _builder_version=”4.10.4″ _module_preset=”default” global_colors_info=”{}”]\n
1 Overview<\/h2>\n
1.1 Learning Objectives<\/h3>\n
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2 About the Project<\/h2>\n
3 Your Implementation<\/h2>\n
3.1 Getting Started<\/h3>\n
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3.2 Task & Requirements<\/span><\/h3>\n
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