[et_pb_section fb_built=”1″ admin_label=”Section” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_row admin_label=”Project Title” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”fdd234b2-33f3-4b45-8c11-1d835f66535b” 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=”4.16″ _module_preset=”default” 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=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_divider color=”#eeeeee” divider_position=”center” divider_weight=”3px” _builder_version=”4.16″ _module_preset=”default” 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=”4.16″ _module_preset=”default” 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=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.16″ _module_preset=”default” header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#overview” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”%22||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”About the Project” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.16″ _module_preset=”default” 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=”%22||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=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.16″ _module_preset=”default” header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#implementation” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”%22||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”Contents of Report” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.16″ _module_preset=”default” 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=”%22||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=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.16″ _module_preset=”default” header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#testing” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”%22||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”Submission Requirements” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.16″ _module_preset=”default” 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=”%22||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=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.16″ _module_preset=”default” header_font_size=”16px” header_line_height=”2em” image_icon_custom_padding=”8px|8px|8px|8px|false|false” custom_margin=”|||118px|false|false” custom_padding=”10px|||0px|false|false” link_option_url=”#grading” border_radii_image=”on|100%|100%|100%|100%” border_width_all_image=”2px” border_color_all_image=”#000000″ icon_font_size=”16px” use_circle=”on” use_circle_border=”on” circle_border_color=”#b856c7″ circle_color=”#FFFFFF” global_colors_info=”{}” font_icon__hover_enabled=”on|hover” font_icon__hover=”%22||divi||400″ custom_padding__hover=”|||10px|false|false” custom_padding__hover_enabled=”on|hover” image_icon_background_color__sticky_enabled=”#7EBEC5″ image_icon_background_color__sticky=”#7EBEC5″][\/et_pb_blurb][et_pb_blurb title=”Development Guidelines” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#000000″ image_icon_background_color=”#FFFFFF” icon_placement=”left” image_icon_width=”16px” content_max_width=”100%” _builder_version=”4.16″ _module_preset=”default” 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=”%22||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=”4.16″ _module_preset=”default” 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=”%22||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=”4.16″ _module_preset=”default” 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=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text admin_label=”Text” _builder_version=”4.17.4″ _module_preset=”fdd234b2-33f3-4b45-8c11-1d835f66535b” global_colors_info=”{}”]<\/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 admin_label=”1 Overview” module_id=”overview” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”fdd234b2-33f3-4b45-8c11-1d835f66535b” global_colors_info=”{}”]<\/p>\n In this assignment, you will generate data that you believe will work better for one learner than another. This will test your understanding of the strengths and weaknesses of various learners. You will submit the code for the project in <\/span>Gradescope<\/span> SUBMISSION. There is no report associated with this assignment.<\/span><\/p>\n [\/et_pb_text][et_pb_text admin_label=”1.1 Learning Objectives” _builder_version=”4.16″ _module_preset=”fdd234b2-33f3-4b45-8c11-1d835f66535b” 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=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”fdd234b2-33f3-4b45-8c11-1d835f66535b” global_colors_info=”{}”]<\/p>\n In this project, you will evaluate the relative strengths of two types of supervised learners: a linear regression learner and a decision tree learner. Specifically, you will write a module that generates datasets that will be used by the learners. Your goal is to 1) produce datasets that enable a linear regression learner to consistently outperform a decision tree learner and 2) produce datasets that enable a decision tree learner to consistently outperform a linear regression learner.<\/span>\u00a0<\/span><\/p>\n Your data generation implementation should use a random number generator as part of its data generation process. We will pass your generators a random number seed. Whenever the seed is the same you should return exactly the same data set. Different seeds should result in different data sets. This project may require readings or additional research to ensure an understanding of the relative strengths and weaknesses of the different types of learners.<\/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=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.18.0″ _module_preset=”fdd234b2-33f3-4b45-8c11-1d835f66535b” global_colors_info=”{}”]<\/p>\n You will create a Python program called gen_data.py, which contains three functions according to this <\/span>API specification<\/span><\/a>. The DTLearner and LinRegLearner specification are also provided in the API specification as a reference. This gen_data.py file must implement the three functions given in the API specification:<\/span>\u00a0<\/span><\/p>\n Your data generation should use a random number generator as part of its data generation process. We will pass your generators a random number seed (as shown below). Whenever the seed is the same you must return exactly the same data set. Different seeds must result in different data sets.<\/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=”4.18.0″ _module_preset=”fdd234b2-33f3-4b45-8c11-1d835f66535b” 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\u202f4\u202fcan be obtained\u202ffrom:\u202f<\/span><\/span>Defeat_Learners_2022Fall.zip<\/a>.\u202f<\/span><\/span>\u00a0<\/span><\/p>\n Extract its contents into the base directory (e.g., ML4T_2022Fall). This will add a new folder called \u201c<\/span>defeat_learners<\/span>\u201d to the course <\/span>directory<\/span> structure.\u202f<\/span><\/span>\u00a0<\/span><\/span><\/p>\n [\/et_pb_text][et_pb_image src=”http:\/\/lucylabs.gatech.edu\/ml4t\/wp-content\/uploads\/2021\/08\/Screen-Shot-2021-08-24-at-8.18.13-PM.png” title_text=”Screen Shot 2021-08-24 at 8.18.13 PM” _builder_version=”4.16″ _module_preset=”bfd57e0e-12e9-4ae6-be9f-6feca129409e” global_colors_info=”{}”][\/et_pb_image][et_pb_text admin_label=”3.1 Getting Started – Cont” _builder_version=”4.16″ _module_preset=”fdd234b2-33f3-4b45-8c11-1d835f66535b” global_colors_info=”{}”]<\/p>\n \u202fWithin the defeat_learners folder are several files:\u00a0<\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_text admin_label=”3.2 Task & Requirements” _builder_version=”4.16″ _module_preset=”fdd234b2-33f3-4b45-8c11-1d835f66535b” global_colors_info=”{}”]<\/p>\n Implement the <\/span>best_4_lin_reg()<\/span><\/b> function such that when the generated dataset is passed to both learners, the LinRegLearner performs significantly better than the DTLearner (see rubric below for specific performance details).<\/span>\u00a0<\/span><\/p>\n Also implement the <\/span>best_4_dt()<\/span><\/b> function such that when the generated dataset is passed to both learners, the DTLearner performs significantly better than the LinRegLearner (see rubric below for specific performance details).<\/span>\u00a0<\/span><\/p>\n Each dataset must include no fewer than 2 and no more than 10 features (or \u201cX\u201d) columns. The dataset must contain 1 target (or \u201cY\u201d) column. The Y column must contain real numbers.\u00a0Y values may not be hard-coded and must be generated by the X value.\u00a0<\/span><\/span><\/span>Each dataset must contain no fewer than 10 and no more than 1000 examples (i.e., rows). While you are free to determine these sizes, they may not vary between generated datasets.<\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_text admin_label=”3.2.1 Example” _builder_version=”4.16″ _module_preset=”fdd234b2-33f3-4b45-8c11-1d835f66535b” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_code _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]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
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3.2\u00a0Construct the Best for Linear Regression Learner and Decision Tree Learner Datasets<\/h3>\n
3.2.1 Example\u00a0<\/span><\/h4>\n