we are going to use linear programming to develop a simple machinelearning algorithm to help us classify data points. Training data and Test data.Each of the 20 data points in the training data consists of 2 sensor readings x1, x2 whichare real numbers corresponding to the readout of two sensors during an event and a classi-cation y which is either 0 or 1, 0 indicates the event corresponding to the sensor data wasdetermined to not be a gravitational wave and a 1 indicates the event was determined tobe a gravitational wave. Thus, a typical line in the le looks something like:0.0 78.1 60.6Which indicates the 2 sensors had readings 78.1, 60.6 respectively, and the 0.0 indicatesno gravitational wave was observed. The test data consists again of sensor readings xi but with no classication y provided.Your job is to use the training data to develop a model that can take in sensor data andpredict the classication (again, 0 or 1). You will then run your model on the 20 points inthe test data to classify it. The true classication will then be provided separately so youcan check how well your model did against the true results.1.3 RequirementsYour work is to be submitted as a report that would be appropriate to send to LIGO.Such a report needs to include at a minimum:1. Relevant background information someone might need to understand how your modelworks2. A description of the model itself3. Results and analysis of how well the model performed4. Any discussion, gures, or code that you think would be pertinent to includeThere is no page requirement, but enough information needs to be included so thatsomeone with little to no previous knowledge about what you are doing can follow yourwork.

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter9: Systems Of Equations And Inequalities

Section9.5: Systems Of Linear Equations In More Than Two Variables

Problem 27E

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Related questions

Question

we are going to use linear programming to develop a simple machine
learning algorithm to help us classify data points.

Training data and Test data.
Each of the 20 data points in the training data consists of 2 sensor readings x1, x2 which
are real numbers corresponding to the readout of two sensors during an event and a classi-
cation y which is either 0 or 1, 0 indicates the event corresponding to the sensor data was
determined to not be a gravitational wave and a 1 indicates the event was determined to
be a gravitational wave. Thus, a typical line in the le looks something like:
0.0 78.1 60.6

Which indicates the 2 sensors had readings 78.1, 60.6 respectively, and the 0.0 indicates
no gravitational wave was observed.

The test data consists again of sensor readings xi but with no classication y provided.
Your job is to use the training data to develop a model that can take in sensor data and
predict the classication (again, 0 or 1). You will then run your model on the 20 points in
the test data to classify it. The true classication will then be provided separately so you
can check how well your model did against the true results.
1.3 Requirements
Your work is to be submitted as a report that would be appropriate to send to LIGO.
Such a report needs to include at a minimum:
1. Relevant background information someone might need to understand how your model
works
2. A description of the model itself
3. Results and analysis of how well the model performed
4. Any discussion, gures, or code that you think would be pertinent to include
There is no page requirement, but enough information needs to be included so that
someone with little to no previous knowledge about what you are doing can follow your
work.