this is programming and this is R language Question 1 Build a decision tree classifier to classify diabetes dataset Calculate the accuracy, and confusion matrix for the model If the measurement of Glucose, insulin and sspg for a person are 100, 1050, 200 respectively. Create a tibble representing this data and use the created model to check the category of this person Question 2 Repeat question 2 part (a) using Random Forest model instead of single decision tree. Calculate the accuracy of the new created model. Compare the accuracy obtained in the two models. Comment on that
this is programming and this is R language Question 1 Build a decision tree classifier to classify diabetes dataset Calculate the accuracy, and confusion matrix for the model If the measurement of Glucose, insulin and sspg for a person are 100, 1050, 200 respectively. Create a tibble representing this data and use the created model to check the category of this person Question 2 Repeat question 2 part (a) using Random Forest model instead of single decision tree. Calculate the accuracy of the new created model. Compare the accuracy obtained in the two models. Comment on that
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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Question 1
- Build a decision tree classifier to classify diabetes dataset
- Calculate the accuracy, and confusion matrix for the model
- If the measurement of Glucose, insulin and sspg for a person are 100, 1050, 200 respectively. Create a tibble representing this data and use the created model to check the category of this person
Question 2
- Repeat question 2 part (a) using Random Forest model instead of single decision tree.
- Calculate the accuracy of the new created model.
- Compare the accuracy obtained in the two models. Comment on that
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