Logistic regression is a classification algorithm used to predict binary outcomes based on one or more predictor variables. It employs the logit function to estimate the probability of an event, with wide applications in fields like medicine and marketing. While it is easy to implement and interpret, it struggles with a high number of categorical features and highly correlated independent variables.

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