Binary classification with logistic regression algorithm using hadoop

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The document presents a discussion on binary classification using the logistic regression algorithm in a Hadoop environment. It includes an introduction to the logistic regression concepts, optimization of the logistic function, and analysis of a diabetes dataset where outputs are binary indicators of health status. The focus is on utilizing attributes to predict the likelihood of diabetes based on historical patient data.

Engineering

Binary classification with Logistic Regression
Algorithm using Hadoop
Presenter
Sajib Sen
1

Outline:
• Brief Introduction of Logistic Regression
• An ideal solution
• Design
• Analysis

Brief Introduction of Logistic Regression

Brief Introduction of Logistic Regression(cont..)
Mathematical explanation:
Hypothesis, ℎθ(x) = θ
𝑇
𝑥
Logistic function, f(z) = 1/1+ 𝑒− 𝑧
, where z= θ
𝑇
𝑥
Hypothesis for binary classification using logistic function
ℎθ(x) = 1/1+ 𝑒−θ
𝑇
𝑥
Optimizing the logistic function
θ𝑗:= θ𝑗 - α
𝜕𝐽(θ)
𝜕θ𝑗
, where 𝐽(θ) is the cost function

Analysis
The dataset is for those women who are disgnosed for diabetes. Each row is an input and each column is
the attribute for the input except the last column is the output. The output is in binary, where 0 means
tested negative for diabetes and 1 for positive.
1 85 66 29 0 26.6 0.351 31 0
8 183 64 0 0 23.3 0.672 32 1

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Binary classification with logistic regression algorithm using hadoop

1. Binary classification with Logistic Regression Algorithm using Hadoop Presenter Sajib Sen 1

2. Outline: • Brief Introduction of Logistic Regression • An ideal solution • Design • Analysis

3. Brief Introduction of Logistic Regression

4. Brief Introduction of Logistic Regression(cont..) Mathematical explanation: Hypothesis, ℎθ(x) = θ 𝑇 𝑥 Logistic function, f(z) = 1/1+ 𝑒− 𝑧 , where z= θ 𝑇 𝑥 Hypothesis for binary classification using logistic function ℎθ(x) = 1/1+ 𝑒−θ 𝑇 𝑥 Optimizing the logistic function θ𝑗:= θ𝑗 - α 𝜕𝐽(θ) 𝜕θ𝑗 , where 𝐽(θ) is the cost function

5. An Ideal Solution

6. Design

7. Design(cont..)

8. Design(cont..)

9. Analysis The dataset is for those women who are disgnosed for diabetes. Each row is an input and each column is the attribute for the input except the last column is the output. The output is in binary, where 0 means tested negative for diabetes and 1 for positive. 1 85 66 29 0 26.6 0.351 31 0 8 183 64 0 0 23.3 0.672 32 1

10. Analysis(cont..)

11. Analysis(cont..)

12. Thank you 4/30/18 12