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Q1 Classification The classification tree was trained with response data Y and using two explanatory variables X1, X2. Yes X20.310283 X10.606056 No X2 0.697338 Yes Yes ## A) Sketch the classification implied by the tree in the region [0,1] 2. Use "Yes" /1 and "No"/0. B) A new set of points has been provided with fresh response values, as follows. x1 x2 Y ## [1,] 0.25 0.33 1 ## [2,] 0.50 0.33 0 ## [3,] 0.75 0.33 0 ## [4,] 0.25 0.67 0 ## [5,] 0.50 0.67 0 ## [6,] 0.75 0.67 1 Using the data and the trained tree, give predicted (0/1) labels for the new points. C) Report the confusion matrix and performance measures TPR=| on the performance of this classifier. and FPR= Briefly comment D) Can the given trained tree be trimmed to a simpler version, removing redundancies? E) Write a different tree that is equivalent to the given one.

Q1 Classification The classification tree was trained with response data Y and using two explanatory variables X1, X2. Yes X20.310283 X10.606056 No X2 0.697338 Yes Yes ## A) Sketch the classification implied by the tree in the region [0,1] 2. Use "Yes" /1 and "No"/0. B) A new set of points has been provided with fresh response values, as follows. x1 x2 Y ## [1,] 0.25 0.33 1 ## [2,] 0.50 0.33 0 ## [3,] 0.75 0.33 0 ## [4,] 0.25 0.67 0 ## [5,] 0.50 0.67 0 ## [6,] 0.75 0.67 1 Using the data and the trained tree, give predicted (0/1) labels for the new points. C) Report the confusion matrix and performance measures TPR=| on the performance of this classifier. and FPR= Briefly comment D) Can the given trained tree be trimmed to a simpler version, removing redundancies? E) Write a different tree that is equivalent to the given one.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Q1 Classification The classification tree was trained with response data Y and using two explanatory variables X1, X2. Yes X20.310283 X10.606056 No X2 0.697338 Yes Yes ## A) Sketch the classification implied by the tree in the region [0,1] 2. Use "Yes" /1 and "No"/0. B) A new set of points has been provided with fresh response values, as follows. x1 x2 Y ## [1,] 0.25 0.33 1 ## [2,] 0.50 0.33 0 ## [3,] 0.75 0.33 0 ## [4,] 0.25 0.67 0 ## [5,] 0.50 0.67 0 ## [6,] 0.75 0.67 1 Using the data and the trained tree, give predicted (0/1) labels for the new points. C) Report the confusion matrix and performance measures TPR=| on the performance of this classifier. and FPR= Briefly comment D) Can the given trained tree be trimmed to a simpler version, removing redundancies? E) Write a different tree that is equivalent to the given one.
Transcribed Image Text:Q1 Classification The classification tree was trained with response data Y and using two explanatory variables X1, X2. Yes X20.310283 X10.606056 No X2 0.697338 Yes Yes ## A) Sketch the classification implied by the tree in the region [0,1] 2. Use "Yes" /1 and "No"/0. B) A new set of points has been provided with fresh response values, as follows. x1 x2 Y ## [1,] 0.25 0.33 1 ## [2,] 0.50 0.33 0 ## [3,] 0.75 0.33 0 ## [4,] 0.25 0.67 0 ## [5,] 0.50 0.67 0 ## [6,] 0.75 0.67 1 Using the data and the trained tree, give predicted (0/1) labels for the new points. C) Report the confusion matrix and performance measures TPR=| on the performance of this classifier. and FPR= Briefly comment D) Can the given trained tree be trimmed to a simpler version, removing redundancies? E) Write a different tree that is equivalent to the given one.
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