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Given the following picture of the data, a decision tree will likely fit the data better than a logistic regression model. 2. -2 -1 2 -2 X, X, True False X2 -2 -1 X2 -2 -1

Given the following picture of the data, a decision tree will likely fit the data better than a logistic regression model. 2. -2 -1 2 -2 X, X, True False X2 -2 -1 X2 -2 -1

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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### Decision Trees vs. Logistic Regression Models #### Analysis of Graphs The provided image displays two graphs comparing different machine learning models' fit for the same dataset in a 2-dimensional space. The x-axes are labeled \( X_1 \) and the y-axes are labeled \( X_2 \). 1. **First Graph (Left Panel):** - The graph portrays a data classification scenario. - It uses a diagonal boundary to separate the two regions. - The regions indicate different classes; the green area represents one class and the yellow area represents another. - This kind of separation boundary is indicative of Logistic Regression, a linear classifier known to create a linear boundary. 2. **Second Graph (Right Panel):** - This graph illustrates the same classification scenario but with a different separation method. - In this graph, the data points are separated using horizontal and vertical lines forming a step-wise region. - This type of step-like boundary suggests the use of a Decision Tree, which segments the data space into rectangular regions. Given the block-like structure of the decision boundaries in the right panel, it can be inferred that a Decision Tree model has been used, which is known for creating non-linear boundaries by splitting the data space axis-aligned lines. This rectangular segmentation is often better at capturing complex patterns in data compared to a linear model like Logistic Regression, which is constrained to linear boundaries. #### True or False Statement Given the following picture of the data, a Decision Tree will likely fit the data better than a Logistic Regression model. - **Answer: True** This reflects the information in the graphs that indicate non-linear boundary fitting capabilities of a Decision Tree compared to a simple linear boundary from Logistic Regression. #### Multiple Choice: - **True**: A decision tree will likely fit the data better considering the graphical representation. - False
Transcribed Image Text:### Decision Trees vs. Logistic Regression Models #### Analysis of Graphs The provided image displays two graphs comparing different machine learning models' fit for the same dataset in a 2-dimensional space. The x-axes are labeled \( X_1 \) and the y-axes are labeled \( X_2 \). 1. **First Graph (Left Panel):** - The graph portrays a data classification scenario. - It uses a diagonal boundary to separate the two regions. - The regions indicate different classes; the green area represents one class and the yellow area represents another. - This kind of separation boundary is indicative of Logistic Regression, a linear classifier known to create a linear boundary. 2. **Second Graph (Right Panel):** - This graph illustrates the same classification scenario but with a different separation method. - In this graph, the data points are separated using horizontal and vertical lines forming a step-wise region. - This type of step-like boundary suggests the use of a Decision Tree, which segments the data space into rectangular regions. Given the block-like structure of the decision boundaries in the right panel, it can be inferred that a Decision Tree model has been used, which is known for creating non-linear boundaries by splitting the data space axis-aligned lines. This rectangular segmentation is often better at capturing complex patterns in data compared to a linear model like Logistic Regression, which is constrained to linear boundaries. #### True or False Statement Given the following picture of the data, a Decision Tree will likely fit the data better than a Logistic Regression model. - **Answer: True** This reflects the information in the graphs that indicate non-linear boundary fitting capabilities of a Decision Tree compared to a simple linear boundary from Logistic Regression. #### Multiple Choice: - **True**: A decision tree will likely fit the data better considering the graphical representation. - False
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