Logistic Regression : classification algorithm

Regression is one of the most common models of machine learning .
Logistic regression is a classification algorithm

1. Logistic
Regression

2. Introduction
logistic Regression : what , when and
how ?
Advantages and Disadvantages
Uses cases
Demo

3. Introduction

What is Regression?
Regression is one of the most common models of machine learning , it is a way of
predicting future happenings between a dependent (target) and one or more
independent (predictor) variables .


3
Regression
Linear regression Logistic regression
Polynomial
regression

4. logistic Regression :
What ?
Logistic regression is a classification algorithm used to assign observations to a discrete set of
classes , In other words,the outcome or target variable is dichotomous in nature , which means it
can only have one of two values (either 0 or 1, true or false, black or white, spam or not spam, and so
on … )

5. logistic Regression :
When ?

6. logistic Regression :
How ?
Logistic Regression predicts the probability of occurrence of a binary event utilizing what we call
logit function or Sigmoid function .




The hypothesis of logistic regression :

If the curve goes to positive infinity, y predicted will become 1, and if the curve goes to negative
infinity, y predicted will become 0.

7. The hypothesis of logistic regression :
1.Linear Regression fonction :


1.Sigmoid Function:






1.Apply Sigmoid function on linear regression:

8. Example

Does a patient have a lung cancer ?

9. New Data

10. Best Fit
Maximum likelihood

11. Optimization
The Cost function of Logistic regression
Cost Function

The cost function represents optimization objective i.e. we create a cost function
and minimize it so that we can develop an accurate model with minimum error.

12. Gradient Descent
Gradient descent is an optimization algorithm ,
The main goal of it is to minimize the cost value.
Objective: To minimize the cost function we have to run the gradient
descent function on each parameter .




12

13. Uses Cases
Logistic Regression has a wide range of real-life applications :

medical fields
13



Social sciences


Marketing fields

14. Advantages
❖Useful on problems where you need to give
more rationale for a prediction
❖Doesn't require high computation power
❖Easy to implement and to interpret
❖Used widely by data analyst and scientist
❖Logistic regression provides a probability
score for observations

15. Disadvantages
❖Logistic regression is not able to handle a
large number of categorical features.

❖Logistic regression will not perform well
with independent variables that are not
correlated to the target variable and are
very similar or correlated to each other.

16. “
▸Thank you !
16