Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification)

Summary

This tutorial introduces logistic regression as a technique for solving classification problems, where the prediction is categorical rather than continuous as in linear regression. The video explains the concept of binary and multi-class classification, using the example of predicting customer insurance purchases based on age. It demonstrates how to visualize data with a scatter plot and how linear regression might be inappropriate for certain datasets. The presenter then introduces the sigmoid function, which logistic regression uses to model the probability of a certain class. The tutorial continues with a practical example using a dataset, showing how to perform a train-test split, train a logistic regression model, make predictions, and evaluate the model's accuracy. Finally, the video concludes with an exercise for viewers to apply logistic regression to an HR Analytics dataset to predict employee retention.

Takeaways

• 📈 The tutorial aims to solve a simple classification problem using logistic regression, which is different from linear regression that predicts continuous values.
• 🔍 Classification problems predict categorical outcomes, such as yes/no or choosing among multiple categories.
• 📊 Binary classification involves predicting an outcome with only two categories, while multi-class classification deals with more than two categories.
• 📉 The script demonstrates using a scatter plot to visualize data distribution, which helps in identifying patterns in the data before applying logistic regression.
• 🤖 Logistic regression models use a sigmoid function to transform linear equation outputs into a probability range between 0 and 1.
• 🧮 The sigmoid function has an S-shaped curve, mathematically represented as 1 / (1 + e^(-z)), where 'e' is Euler's number.
• 📝 The tutorial covers how to implement logistic regression using the scikit-learn library in Python, abstracting the complex mathematics.
• ⏭️ The process includes data splitting into training and test sets, model training with the training set, and making predictions with the test set.
• 💯 The accuracy of the logistic regression model is evaluated using the test set, with a score close to 1 indicating a high accuracy for the given dataset.
• 🤓 The script suggests exploring Kaggle for various datasets to practice building logistic regression models and solving real-world problems.
• 📚 The exercise at the end of the tutorial challenges learners to apply logistic regression to an HR Analytics dataset to predict employee retention.
• 🔧 The exercise involves exploratory data analysis, plotting bar charts for salary and department impact, building a logistic regression model, making predictions, and measuring model accuracy.

Q & A

What is the main goal of the tutorial?

The main goal of the tutorial is to solve a simple classification problem using logistic regression.

What is the difference between linear regression and classification problems?

Linear regression is used to predict continuous values, such as home prices or stock prices, while classification problems predict categorical values, such as yes/no or selecting one category from multiple options.

What are the two types of classification problems mentioned in the script?

The two types of classification problems mentioned are binary classification, which involves predicting a simple yes or no outcome, and multi-class classification, which involves predicting one category from more than two available options.

How does logistic regression differ from linear regression in terms of the output it provides?

Logistic regression provides an output that is a probability ranging between 0 and 1, which can be used to classify the prediction into categories, whereas linear regression provides a continuous output that can be any number.

What is the sigmoid function and how is it used in logistic regression?

The sigmoid function is a mathematical function that takes any input and transforms it into a value between 0 and 1. It is used in logistic regression to convert the linear equation's output into a probability score that can be used for classification.

What is the purpose of splitting the dataset into a training set and a test set?

The purpose of splitting the dataset is to use the majority of the data (training set) to train the model and a smaller portion (test set) to evaluate its performance and ensure that it generalizes well to new, unseen data.

How does the logistic regression model make predictions?

The logistic regression model makes predictions by applying the sigmoid function to a linear equation derived from the training data. The output of the sigmoid function is then used to classify the prediction into one of the categories.

What is the significance of the score returned by the logistic regression model?

The score returned by the logistic regression model represents the accuracy of the model. It is a measure of how well the model's predictions match the actual outcomes in the test set.

How can the logistic regression model predict the probability of an event occurring?

The logistic regression model can predict the probability of an event occurring by applying the sigmoid function to the linear equation's output. The resulting probability score indicates the likelihood of the event.

What is the purpose of exploratory data analysis in the context of the HR Analytics dataset?

The purpose of exploratory data analysis is to identify patterns and relationships within the data that can help understand factors affecting employee retention or attrition. This can inform the development of a logistic regression model to predict employee retention.

What are the steps involved in building a logistic regression model for the HR Analytics dataset?

The steps involved include: 1) Exploratory data analysis to identify key factors affecting employee retention, 2) Plotting bar charts to visualize the impact of factors like salary and department on retention, 3) Building a logistic regression model using the identified factors, 4) Making predictions with the model, and 5) Measuring the model's accuracy.

Outlines

00:00

📊 Introduction to Logistic Regression for Classification Problems

The video begins by contrasting logistic regression with linear regression. While linear regression is used for predicting continuous values, logistic regression is introduced as a method for solving classification problems, which involve predicting categorical outcomes. The tutorial aims to address binary classification, where the outcome is a simple yes or no, and multi-class classification, where there are more than two categories to predict. An example scenario is given where a data scientist is tasked with predicting whether a potential customer will buy life insurance based on their age. The importance of plotting data and observing patterns is emphasized before introducing logistic regression as the solution for such predictive tasks.

05:01

📈 Fitting a Logistic Regression Model to Insurance Data

The paragraph explains the process of using logistic regression to model the likelihood of a customer buying insurance based on their age. It discusses the limitations of linear regression in classification tasks and introduces the sigmoid function as a method to transform a linear equation's output into a probability value between 0 and 1. The sigmoid function is mathematically defined, and its S-shaped curve is described. The video then demonstrates how to implement logistic regression using a library like scikit-learn, abstracting away the complex mathematics. The process includes loading data, plotting a scatter plot to visualize the data distribution, and splitting the dataset into training and test sets. The logistic regression model is trained using the training set, and its accuracy is evaluated using the test set.

10:06

🤖 Training the Logistic Regression Model and Making Predictions

This section details the steps to train a logistic regression model using the training data and then make predictions on the test data. The model's predictions are binary, indicating whether the customer will buy insurance (1) or not (0). The accuracy of the model is assessed by comparing its predictions to the actual outcomes, and the model's score接近 1, indicating near-perfect accuracy. However, the presenter notes that this high score is partly due to the small dataset size. The paragraph also covers how to predict the probability of an outcome using the model, which provides a more nuanced understanding of the prediction's certainty.

15:08

📚 Exercise: Applying Logistic Regression to HR Analytics

The final paragraph transitions into an exercise where viewers are encouraged to apply logistic regression to a real-world dataset focusing on employee retention rates. The task involves exploratory data analysis to identify factors affecting employee retention, plotting bar charts to visualize the impact of salary and department on retention, and building a logistic regression model to predict employee attrition. The exercise aims to help HR departments focus on specific areas to improve employee retention. The video concludes with a prompt for viewers to share their findings in the comments and to attempt the exercise independently before consulting the provided answers.

Keywords

💡Logistic Regression

Logistic regression is a statistical method used for binary classification problems, which involves predicting categorical outcomes. In the script, logistic regression is introduced as a suitable technique for predicting outcomes like buying insurance based on age, differing from linear regression which predicts continuous values. The tutorial details how logistic regression uses a sigmoid function to map predicted values into a binary format, making it ideal for scenarios where the outcomes are either 'yes' or 'no'.

💡Binary Classification

Binary classification is a type of classification problem where there are only two possible outcomes. The script refers to binary classification in the context of determining whether a customer will buy insurance or not, with outcomes being 'yes' or 'no'. This concept is foundational in understanding logistic regression, which the tutorial employs to handle such binary outcomes.

💡Sigmoid Function

The sigmoid function is a mathematical function that outputs a value between 0 and 1, which is crucial in logistic regression to convert linear equation outputs into probabilities. In the script, the sigmoid function is described in the logistic regression section where it's used to compress the output of a linear equation (Z), facilitating the model's ability to classify binary outcomes effectively.

💡Linear Regression

Linear regression is a predictive modeling technique for estimating a continuous dependent variable based on one or more independent variables. The script discusses linear regression in the context of predicting home prices and contrasts it with logistic regression, pointing out its inadequacy for categorical outcomes like those in binary classification problems.

💡Outliers

Outliers are data points that differ significantly from other observations. In the tutorial, outliers are mentioned in the context of fitting a logistic regression model, where they can adversely affect the accuracy of a linear regression line when predicting categorical outcomes, demonstrating the need for logistic regression in certain cases.

💡Model Training

Model training in machine learning involves developing a model based on provided data so that it can make accurate predictions. In the script, training a logistic regression model involves using historical data (age and insurance buying behavior) to fit the model, which is then used to predict whether new customers will buy insurance.

💡Test Size

Test size refers to the proportion of the data set aside for testing the machine learning model's accuracy after training. The script details using a split of 90% training data and 10% test data, demonstrating how to divide data to evaluate the effectiveness of a logistic regression model in predicting new instances.

💡Predictive Model

A predictive model is an algorithm that uses historical data to predict outcomes. The script frequently mentions building and using predictive models, specifically logistic regression, to estimate whether individuals of certain ages are likely to purchase insurance based on past data.

💡Data Set

A data set in the context of the video script is a collection of data used for training and testing a machine learning model. The script provides an example of a data set containing ages and insurance purchasing records, which is used to train and test the logistic regression model.

💡Accuracy

Accuracy in the context of machine learning models refers to the proportion of correct predictions made by the model. In the script, the accuracy of the logistic regression model is assessed after training and testing, with the script highlighting a high accuracy score due to the small size of the data set, which might not be indicative of performance with larger, more complex data sets.

Highlights

The tutorial aims to solve a simple classification problem using logistic regression, contrasting with linear regression which predicts continuous values.

Classification problems predict categorical outcomes, such as yes/no or choosing among multiple options.

Binary classification predicts a simple yes or no outcome, while multi-class classification involves more than two categories.

Logistic regression is introduced as a technique to solve classification problems, different from linear regression.

The tutorial provides a real-world example of predicting customer insurance purchase likelihood based on age.

A scatter plot is used to visualize data distribution and identify patterns before applying logistic regression.

Linear regression is shown to be inadequate for classification problems with non-linear data.

The sigmoid or logit function is explained as a mathematical tool that logistic regression uses to model probabilities.

The sigmoid function maps any input to a range between 0 and 1, creating an S-shaped curve.

Logistic regression combines a linear equation with a sigmoid function to predict the likelihood of a categorical outcome.

The tutorial demonstrates using the scikit-learn library to implement logistic regression without manually coding the mathematics.

Data is split into training and test sets using the train_test_split method for model evaluation.

The logistic regression model is trained using the training data and then used to make predictions on the test data.

The model's accuracy is assessed using the test data and the score method, which returns a value between 0 and 1.

The tutorial also covers predicting the probability of an outcome using the logistic regression model.

An exercise is provided to apply logistic regression to an HR Analytics dataset for predicting employee retention.

The exercise encourages exploratory data analysis to identify factors affecting employee retention.

Participants are guided to build a logistic regression model, make predictions, and measure the model's accuracy.

The tutorial concludes with a call to action for viewers to attempt the exercise and think critically about the solutions.

Transcripts

the goal of this tutorial is to solve a

simple classification problem using

logistic regression if you followed my

previous tutorial we have learnt a lot

about linear regression especially the

home prices example linear regression

can be used to predict other things such

as weather and stock prices and in all

these examples the prediction value is

continuous there are other type of

problems such as predicting weather

email spam or not whether the customer

will buy the life insurance product or

person is going to vote for which party

all these problems if you think about it

the prediction value is categorical

because the thing that you are trying to

predict is one of the available

categories in the first two example it

is simple yes or no answer in the third

examples it is one of the available

categories whereas in case of linear

regression the home prices example we

saw that the predicted value could be

any number it is not one of the defined

categories okay hence this second type

of problems is called classification

problem and logistic regression is a

technique that is used to solve these

classification problems now in the

classification examples that we saw

there are two types so the first example

was predicting whether customer will buy

insurance or not here the outcome is

simple yes or no this is called binary

classification on the other hand when

you have more than two categories that

example is called multi class

classification

let's say you are working as a data

scientist in a life insurance company

and your boss gives you a task of

predicting how likely a potential

customer is to buy your insurance

product and what you are seeing here is

the available data

and based on the age the information you

have is whether customer bought the

insurance or not now here you can see

some patterns such as young people don't

buy the insurance too much you can see

like there are persons with 20 to 25

these kind of ages where zero means they

didn't buy the insurance whereas as the

persons age increases he's more likely

to buy the insurance so you know the

relationship and you want to build a

machine learning model that can do a

prediction based on the age of a

potential customer so as a data

scientist now this is the job you have

been given now the first thing you would

do when you have this data is you will

plot a scatter plot which looks like

this when you have walked on linear

regression problems already the first

temptation you have in your mind is you

start using linear regression so when

you draw our linear equation line using

the linear regression it will look

something like this now how did we come

up with this line for that you can

follow my previous linear regression

tutorials if you think about it what I

can do here is I can predict the value

using a linear equation line and say

that if my predictive value is more than

0.5 so here this is 0.5 if it is more

than 0.5 then I will say ok customer is

likely to buy the insurance if it is

less than that then he is not going to

buy the insurance so anything on the

right hand side is yes anything on the

left hand side is no now of course we

have these outliers but we don't care

about them too much because for 90% of

the cases of our linear regression will

work ok now imagine you have a data

point which is far on the right-hand

side here so say a customer whose age is

more than 80 years let's say he bought

your insurance ok then your scatterplot

will look like this and your linear

equation

might look like this in this case what

will happen is when I draw a separation

between between the two sections using

0.5 value here the problem arises with

these data points actually the answer

was yes here

but my question predicted them to be no

so you can see that this is pretty bad

when you use linear regression for data

a class like this now here is the most

interesting part imagine you can draw a

line like this this is much better fit

compared to the previous linear equation

that we had okay and here when you draw

a separation between Z using 0.5 value

you can clearly say that this model

works much better than the previous one

the question arises what is this line

exactly and how do you come up with this

right if you have learnt statistics you

might have heard about sigmoid or logit

function and that's what this is okay

now

the moment you hear this term sigmoid

you might pause this video and start

googling about sigmoid and it is fine

you can read all the articles about

Sigma or logic logit function to get

your understanding correct on

mathematics behind it

but if you don't want to do it I will

give you a basic idea the Sigma

functions equation is 1 divided by 1

plus e raised to minus Z where e is some

mathematical constant is called Europe

Euler's numbers the value is this now

think about this equation for a moment

what we are doing here is we are

dividing by 1 by a number which is

slightly greater than 1 and when you

have this situation the outcome will be

less than 1 correct

so all you are doing with this

my function is coming up with a range

which is between zero to one so if you

feed set of numbers to the sigmoid

functions all it will do is convert them

to zero to one range and the equation

that you will get it looks like s-shape

right so if you plot a 2d plot 2d chart

it will look like a shape function that

we saw in the previous slide essentially

what we are doing with logistic

regression is we have a line like this

which is linear equation and you know

the equation for our linear line which

is MX plus B all you're doing is you are

feeding this line into a sigmoid

function and when you do that you

convert this line into this a shape ok

so here you can see that my Z I replace

with MX plus B so I applied sigmoid

function on top of my linear equation

and that's how I got my has shaped line

here all right now all of this math is

just for your understanding as a next

step we are going to write logistic

regression using SQL on library and

these details are abstracted for you so

don't worry about it you don't have to

implement all of this mathematics you

will just make a one simple call and it

will work magically for you all right so

let's get straight into writing the code

here is the CSV file containing the

insurance data you can see there are two

columns age and whether that person

bought the insurance or not and we are

going to import this into our panda's

data frame so I have loaded my Jupiter

notebook by running Jupiter notebook

come on on my command line imported

couple of important libraries and then I

imported the same CSV file into my data

frame which looks like this and now I'm

going to plot

a scatterplot just to see the data

distribution and you can see that I get

a plot like this here these are the

customers who didn't buy the insurance

these are the ones who bought the

insurance and you can see that if the

person is younger he's less likely to

buy the insurance and as the person gets

older he is more likely to buy the

insurance the first thing now we are

going to do is use trained taste split

method to split our data set so if you

look at our data we have 27 rows so we

are going to split these rows into

training set and test set again I have a

separate tutorial for how to do train

and test split so you can watch that it

is basically from Escalon model

selection you import train taste split

method here my X is DF age now I am

doing doing two brackets because the

first parameter is X which has to be

multi-dimensional array so I'm using I'm

just trying to derive a data frame here

and what insurance is why and I will say

what is my taste size if you want to see

the arguments you can do shift tab and

it will show you a help for this

function so I used this a lot

it is pretty useful so let's see so

there is this taste underscore size

parameter so let's use taste and just

core size we are going to do or less

less a train size right so training size

is 0.9 so 90% of the example we are

using for training and 10% we will use

for actually testing over model

now what do you get back as a result so

these are the things you get back as it

is all so I'm just going to copy from

here and that's it hit ctrl enter to run

it okay so here there's some warning

maybe they are asking us to use test

size doesn't matter okay

let's look at our test so what test is

18 23 and 40 so these are the three

values we are going to perform or taste

on when you look at our X train these

are these are the data samples we will

use to train our model all right so

let's now import logistic regression so

from same linear model you can import

logistic regression logistic education

eske lon alright so we even have

logistic regression class in ported and

we are going to create an object of this

class we'll call it a model and that

model now we'll do a training remember

in SQL and whenever you are using this

method fit you are actually doing your

training for your model so X train

invite train this is what you use for

your training when you execute this this

means your your model is trained now and

it is ready to make predictions so what

these three values we are making a

prediction so I will do model dot

product and X paste so here what it is

saying is 0 0 1 which means first two

samples it is saying these two customers

are not going to buy your insurance and

you can see that it's kind of working

because they have age of 18 and 23 year

old and we saw that as the ages the

younger age people do not buy the

insurance whereas I think anything more

than 27 28 to buy so here the age is 40

so the answer was 1 okay if you want to

look at the score score is nothing but

it is showing the accuracy of your model

right so what you're doing is you're

giving X tests invitees and here the

score is 1 which means our model is

perfect now this is happening because

our data size is smaller we have only 27

samples but if you have more wider

samples then it will make mistakes in at

least few samples so your score will be

less than 1 right because of the small

size of our data set the score is pretty

high here

another method to try is you can see

that benefit by the way tab it will show

you all the possible functions that

start with ready okay so here you can

also predict a probability so when you

predict a probability of X test it will

show you a probability of your data

sample being in one class versus the

other the first class here is if

customer will not buy the insurance so

for the age 18 and 23 you can see this

point six percent probability that they

will not buy the insurance whereas for

the person with age forty it is reverse

there is 0.6 percent poverty that he

will buy the insurance and point thirty

nine percent probability that he will

not point thirty nine percent really

it's really thirty nine percent that he

will not buy the insurance if you want

to do one off then you can just do model

predict this six you will buy the

insurance that's why you had one and if

you had something like twenty five he

will not buy the insurance that's why

you get zero so this model that we built

is working back pretty well with

logistic regression that's all I had and

now is the time for exercise so if you

know about Cagle of website this is the

website that hosts different coding

competitions and it has one of the more

important feature which is the data sets

so if you go to this data set section

you can download various data sets based

on the type based on the file type or

you can even search for data set so if

you want to do some Titanic Titanic data

analysis you can search for that

basically you can just explore these

data sets for exercises from this I have

downloaded this HR Analytics data set

where there is an analysis on the

employee retention rate or

employee attrition rate if I open that

CSV file here it looks like this where

based on the satisfaction level there's

no number of projects or average monthly

hours that person has worked on you are

trying to establish the correlation

between those factors and whether person

would leave the form or whether he would

continue with the form these kind of

analytics are very important for HR

department because they want to retain

the employees and if you can build a

machine learning model for HR department

then they can focus on specific areas so

that employees don't leave at the farm

so that's what you're going to do you

are a data scientist you're going to

work for your HR department and give

them a couple of things so I have

mentioned all of those things in the

Jupiter notebook which I have available

in the video description below so if you

open that notebook you will see all the

code that we just went through in this

tutorial and at the end you will find

this exercise section ok so there is a

link here you download the data set if

you don't want to download it the same

level as this notebook there is an

exercise folder so download the CSV from

that and you're going to give answer on

these five questions ok first one is out

of all these parameters that we have you

want to find out which factors affect

the employee retention by doing some

exploratory data analysis you will also

plot bar charts showing the impact of

employees salaries and retention also

plot the bar chart showing the impact of

department and employee retention and

then using the factors that you figured

in step one you will build a logistic

regression model and using the model you

are going to do some prediction in the

end you will measure the accuracy of the

model let's do that exercise in the

comments below let

know your answers and if you want to

verify the answers then I have a

separate notebook at the same level in

exercise folder which has all the

answers but don't look at the answers

directly okay a good student is someone

who tries to find the solution on his

own and then he looks at the answer all

right that's all we had thank you very

much for watching I'll see you next