Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.

1. Machine Learning
With
Logistic Regression
Anuj Saxena
Software Consultant
Knoldus Software LLP

2. Agenda
• Machine Learning – Intro?
• Regression vs. Classification
• Introduction to Logistic Regression
• Learning of Logistic Regression
• Use cases

3. SKYNET

4. Machine learning
● Machine learning is the subfield of computer
science that gives computers the ability to
learn without being programmed.

6. Machine Learning techniques
● Supervised
● Unsupervised
● Reinforcement

7. Supervised Learning techniques
● There are two categories for supervised
learning techniques
– Regression
– Classification

8. Regression
v/s
Classification

10. ● Classification
– Output type: discrete
– Trying to find: a boundary
– Evaluation: accuracy
● Regression
– Output type: continuous
– Trying to find: best fit line
– Evaluation: sum of sqaured errors

13. Logistic Regression

14. Logistic Regression
● A type of classification algorithm
● Based on linear regression to evaluate output
and to minimize the error
● Named after the method it uses to evaluate
the outputs the Logit function

15. What is Logit Function?
● Odds: Chances of happening one event over
chances of this event not happening.
● i.e.

17. ● In previous slide we saw the graph of Logit
function.
● This Logit function is bounded over x (between
0 and 1 for input values) and unbounded over
y-axis (output values)
● But we need y-axis (output values) to be
bounded to classify them.

18. ● Hence we take the inverse of the logit function

20. References
● Machine Learning – Tom Mitchell
● http://www.theprojectspot.com/tutorial-post