You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y=1)and "not spam" is the negative class (y=0). You have trained your classifier and there are m= 1000 examples in the cross-validation set. The chart of predicted class vs. actual classis: Predicted class: 1 Predicted class: 0 Actual class: 1 85 15 For reference: Accuracy - (true positives + true negatives)/(total examples) Precision = (true positives)/(true positives false positives) Recall -(true positives)/(true positives + false negatives) F1 score=(2* precision * recall)/(precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps Use the editor to format your answer Actual class: 0 890 10

You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y=1)and "not spam" is the negative class (y=0). You have trained your classifier and there are m= 1000 examples in the cross-validation set. The chart of predicted class vs. actual classis: Predicted class: 1 Predicted class: 0 Actual class: 1 85 15 For reference: Accuracy - (true positives + true negatives)/(total examples) Precision = (true positives)/(true positives false positives) Recall -(true positives)/(true positives + false negatives) F1 score=(2* precision * recall)/(precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps Use the editor to format your answer Actual class: 0 890 10

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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In RapidMiner, the data type of the dependent variable in a logistic regression model must be __________. text string integer binominal In RapidMiner, a logistic regression model will produce _________ when applied to a scoring data set using the Apply Model operator. A binary class prediction Confidence percentages for the positive and negative dependent variable outcomes A confidence percentage for the positive dependent variable outcome A binary class prediction and confidence percentages for the positive and negative dependent variable outcomes
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How can you recognize whether a model satisfies proportionality andadditivity?
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We want to train a binary Logistic Regression classifier for the given trainingdataset:
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