203.5.1 Neural Networks : A Recap of Logistic Regression

In previous section, we studied about Cross Validation

In this post we will just revise our understanding of how logistic regression works, which can be considered a building block for a neural network.

Contents

Neural network Intuition
Neural network and vocabulary
Neural network algorithm
Math behind neural network algorithm
Building the neural networks
Validating the neural network model
Neural network applications
Image recognition using neural networks

Recap of Logistic Regression

Categorical output YES/NO type
Using the predictor variables to predict the categorical output

LAB: Logistic Regression

Dataset: Emp_Productivity/Emp_Productivity.csv
Filter the data and take a subset from above dataset . Filter condition is Sample_Set<3
Draw a scatter plot that shows Age on X axis and Experience on Y-axis. Try to distinguish the two classes with colors or shapes (visualizing the classes)
Build a logistic regression model to predict Productivity using age and experience
Finally draw the decision boundary for this logistic regression model
Create the confusion matrix
Calculate the accuracy and error rates

Solution

Emp_Productivity_raw <- read.csv("C:\\Amrita\\Datavedi\\Emp_Productivity\\Emp_Productivity.csv")

Filter the data and take a subset from above dataset . Filter condition is Sample_Set<3

Emp_Productivity1<-Emp_Productivity_raw[Emp_Productivity_raw$Sample_Set<3,]

dim(Emp_Productivity1)

## [1] 74  4

names(Emp_Productivity1)

## [1] "Age"          "Experience"   "Productivity" "Sample_Set"

head(Emp_Productivity1)

##    Age Experience Productivity Sample_Set
## 1 20.0        2.3            0          1
## 2 16.2        2.2            0          1
## 3 20.2        1.8            0          1
## 4 18.8        1.4            0          1
## 5 18.9        3.2            0          1
## 6 16.7        3.9            0          1

table(Emp_Productivity1$Productivity)

## 
##  0  1 
## 33 41

Draw a scatter plot that shows Age on X axis and Experience on Y-axis. Try to distinguish the two classes with colors or shapes (visualizing the classes)

library(ggplot2)
ggplot(Emp_Productivity1)+geom_point(aes(x=Age,y=Experience,color=factor(Productivity),shape=factor(Productivity)),size=5)

– Build a logistic regression model to predict Productivity using age and experience

Emp_Productivity_logit<-glm(Productivity~Age+Experience,data=Emp_Productivity1, family=binomial())
Emp_Productivity_logit

## 
## Call:  glm(formula = Productivity ~ Age + Experience, family = binomial(), 
##     data = Emp_Productivity1)
## 
## Coefficients:
## (Intercept)          Age   Experience  
##     -8.9361       0.2763       0.5923  
## 
## Degrees of Freedom: 73 Total (i.e. Null);  71 Residual
## Null Deviance:       101.7 
## Residual Deviance: 46.77     AIC: 52.77

coef(Emp_Productivity_logit)

## (Intercept)         Age  Experience 
##  -8.9361114   0.2762749   0.5923444

slope1 <- coef(Emp_Productivity_logit)[2]/(-coef(Emp_Productivity_logit)[3])
intercept1 <- coef(Emp_Productivity_logit)[1]/(-coef(Emp_Productivity_logit)[3])

Finally draw the decision boundary for this logistic regression model

library(ggplot2)
base<-ggplot(Emp_Productivity1)+geom_point(aes(x=Age,y=Experience,color=factor(Productivity),shape=factor(Productivity)),size=5)
base+geom_abline(intercept = intercept1 , slope = slope1, color = "red", size = 2) #Base is the scatter plot. Then we are adding the decision boundary

– Create the confusion matrix

predicted_values<-round(predict(Emp_Productivity_logit,type="response"),0)
conf_matrix<-table(predicted_values,Emp_Productivity_logit$y)
conf_matrix

##                 
## predicted_values  0  1
##                0 31  2
##                1  2 39

Calculate the accuracy and error rates

accuracy<-(conf_matrix[1,1]+conf_matrix[2,2])/(sum(conf_matrix))
accuracy

## [1] 0.9459459

The next post is about Decision Boundary Logistic Regression.

21st June 2017

203.5.1 Neural Networks : A Recap of Logistic Regression

Before we dig into Neural Networks.

Recap of Logistic Regression

LAB: Logistic Regression

Solution