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5.5. Logistic regression's power lies also in the ability to predict a new observation's class. In order to do that, a cut-off point for the different classes must be established. In this study, an effective treatment of a diseased plant was recorded as (Y = 1) and if not, it was recorded as (Y = 0). The following table shows the predictions by the classification method. True Classification Y = 0 Y = 1 Y = 0 45 10 Predicted Y = 1 17 28 Total 62 38 5.5.1. The above table was created based on the following rule: Predict 1 if în ≥ 0.6 and predict 0 if ft < 0.6, explain this rule.

5.5. Logistic regression's power lies also in the ability to predict a new observation's class. In order to do that, a cut-off point for the different classes must be established. In this study, an effective treatment of a diseased plant was recorded as (Y = 1) and if not, it was recorded as (Y = 0). The following table shows the predictions by the classification method. True Classification Y = 0 Y = 1 Y = 0 45 10 Predicted Y = 1 17 28 Total 62 38 5.5.1. The above table was created based on the following rule: Predict 1 if în ≥ 0.6 and predict 0 if ft < 0.6, explain this rule.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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5.5. Logistic regression's power lies also in the ability to predict a new observation's class. In order to do that, a cut-off point for the different classes must be established. In this study, an effective treatment of a diseased plant was recorded as (Y = 1) and if not, it was recorded as (Y = 0). The following table shows the predictions by the classification method. True Classification Y = 0 Y = 1 Y = 0 45 10 Predicted Ỹ = 1 17 28 Total 62 38 5.5.1. The above table was created based on the following rule: Predict 1 if în ≥ 0.6 and predict 0 if ftn < 0.6, explain this rule.
Transcribed Image Text:5.5. Logistic regression's power lies also in the ability to predict a new observation's class. In order to do that, a cut-off point for the different classes must be established. In this study, an effective treatment of a diseased plant was recorded as (Y = 1) and if not, it was recorded as (Y = 0). The following table shows the predictions by the classification method. True Classification Y = 0 Y = 1 Y = 0 45 10 Predicted Ỹ = 1 17 28 Total 62 38 5.5.1. The above table was created based on the following rule: Predict 1 if în ≥ 0.6 and predict 0 if ftn < 0.6, explain this rule.
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