The ROC curve is used to compare the performance of classifiers, when they can return a prediction between 0 and 1. A threshold (split the data) at different values, of this probability, can be used to compare the sensitivity with the 1-specificity across different operating conditions of the test. Generally, the best classifier has Select one: а. low true positive rate and a low false positive rate O b. high true positive rate and a low false positive rate c. high true positive rate and a high false positive rate d. high sensitivity and low specificity е. low true positive rate and a high false positive rate

The ROC curve is used to compare the performance of classifiers, when they can return a prediction between 0 and 1. A threshold (split the data) at different values, of this probability, can be used to compare the sensitivity with the 1-specificity across different operating conditions of the test. Generally, the best classifier has Select one: а. low true positive rate and a low false positive rate O b. high true positive rate and a low false positive rate c. high true positive rate and a high false positive rate d. high sensitivity and low specificity е. low true positive rate and a high false positive rate

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

Problem 1PE

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This is a Machine Learning question : Given a dataset, (1,+), (7, - ), (2, +), (6, -), (5, +), (9, -), (11, +) You are supposed to find a threshold function that minimizes the error in the given dataset. Threshold functions look like this: f( x | a,b ) = sign(a-x).b where a is a real number and b is in {1,-1}. How many possible values should you consider to solve this problem? What is the value of a and b in the case that minimizes the error? What is the minimum error? Show how you found it.
This is a Machine Learning question : Given a dataset, (1,+), (7, - ), (2, +), (6, -), (5, +), (9, -), (11, +) You are supposed to find a threshold function that minimizes the error in the given dataset. Threshold functions look like this: f( x | a,b ) = sign(a-x).b where a is a real number and b is in {1,-1}. How many possible values should you consider to solve this problem? What is the value of a and b in the case that minimizes the error? What is the minimum error? Show how you found it. Note: Please first sort the dataset, and use the mid-point between two adjacent points as the threshold value to identify the misclassified points. Explain in detail how you have identified the misclassified points using the threshold value and the values of b.
Consider a logistic regression classifier that implements the 2-input OR gate. At iteration t, the parameters are given by w0=0, w1=0, w2=0. Given binary input (x1,x2), output of logistic regression is given by 1/(1+exp(-w0-w1*x1-w2*x2)). What will be value of the loss function at t? What will be the values of w0, w1 and w2 at (t+1) with learning rate ɳ=1?
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Write a program on SAS on Healthcare data set of your choice and do the following forecasting: First exponential smoothening Second Exponential Smoothening