A confusion matrix shows the number of correct and incorrect predictions made by a classification model compared to the actual outcomes (target value) in the data. The matrix is NxN, where N is the number of target values (classes). Here we have a 2x2 matrix with two classes: positive and negative.

Complete the confusion matrix exercise attached. Submit the Excel file that has your work shown in the "Results" tab. Note in the "demo data" tab, I performed the calculations for True Positives, but you will need to complete the calculations for the remaining columns.

File Home Insert Page Layout Formulas Data Review View Automate Help Comments Share ~ & Cut Calibri 11 ~ A A de Wrap Text General Normal Bad Good TIX _ AutoSum APO Paste [B) Copy Fill BIU - MAY LEE Merge & Center Conditional Format as Neutral Calculation Check Cell nsert Delete Format Sort & Find & Sensitivity Add-ins Analyze Format Painter ormatting Table Clear ~ Filter * Select Data Clipboard Font Alignment Number Styles Cells Editing Sensitivity Add-ins D165 X V C D E G H K M N P Q R S T U V W X 1 Data Point Predicted (1=yes, 0=no) Actual True Positive True Negative False Positive False Negative ICUI A W N Co - O O O H H O H O O O H O H H H O O H O H O O O O H E H O O HO Ho o O O P Confusion Matrix Intro Demo Data Results + Ready Xx Accessibility: Investigate - + 100% Type here to search X O 8:48 PM 73.F Sunny ~ 91 (( ) 4/28/2024Comments |S Share ~ File Home Insert Page Layout Formulas Data Review View Automate Help AutoSum Bad ood Ap LO & Cut Calibri 11 ~ A A ap Wrap Text General Normal [B) Copy Fill Conditional Format as Neutral Calculation Check Cell nsert Delete Format Sort & Find & Sensitivity Add-ins Analyze Paste BIU~ ~ MAv EEE EE Merge & Center v $ % 9 Clear ~ Format Painter Formatting * Table Filter * Select Data Number Sensitivity Add-ins Clipboard Font Alignment Styles Cells Editing H14 X V A D E F G H K M N P Q R S T U V W X Y Z AA AE - Fill in all green cells: Confusion Matrix Target VOUI A W N Postive Negative Model Positive Negative 10 11 Accuracy: 12 Positive Predictive Value (Precision): 13 Negative Predictive Value: 4 Sensitivity (Recall) 15 16 17 18 19 20 38 Confusion Matrix Intro Demo Data Results + - + 1009 Ready x Accessibility: Investigate 8:48 PM Type here to search X 73.F Sunny ~ 91 ( ) 4/28/2024Comments Share ~ File Home Insert Page Layout Formulas Data Review View Automate Help _ AutoSum & Cut 11 ~ A" A ap Wrap Text General Normal Bad Good TIX APO Calibri Fill [B) Copy Check Cell nsert Delete Format Sort & Find & Sensitivity Add-ins Analyze Paste BIU MAY Merge & Center $ % 9 08 Conditional Format as Neutral Calculation Clear Filter * Select Data Format Painter ormatting * Table Styles Cells Editing Sensitivity Add-ins Clipboard Font Alignment Number E X V C D E F G H K L M N O P Q R S T U V W X Y B 10 CO V MI UNI A W N Target Confusion Matrix Postive Negative Positive Positive Predictive Value a/(a+b) Model a b Negative d Negative Predictive Value d/(c+d) Sensitivity Specificity Accuracy: (a+d)/(a+b+c+d) 11 a/(a+c) d/(b+d) 12 13 14 15 A confusion matrix shows the number of correct and incorrect predictions made by a classfification 16 model compared to the actual outcomes (target value) in the data. The matrix is NXN, where / is the 17 number of target values (classes). Here we have a 2x2 matrix with two classes: positive and negative. 18 19 20 21 22 23 Accuracy: the proportion of total number of predictions that were correct 24 Positive Predictive Value (Precision): the proportion of positive cases that were correctly identified 25 Negative Predictive Value: the proportion of negative cases that were correctly identified 26 Sensitivity (Recall): the proportion of actual postive cases that were correctly identified 27 Specificity: the proportion of actual negative cases that were correctly identified 28 29 30 31 32 33 34 35 36 37 38 Confusion Matrix Intro Demo Data Results + - + 100% Ready 1% Accessibility: Investigate 8:48 PM Type here to search w S X O 73.F Sunny ~ 9 (1 ) 4/28/2024