Lecture 1
Linear Model Classification: Logistic Regression
⭐ Introduction
Logistic Regression is a supervised machine learning
algorithm used for classification problems. It predicts
the probability that a given input belongs to a specific
class (0 or 1).
It is widely used in real-world applications like:
Spam detection
Medical diagnosis
Credit approval
📌 What is Logistic Regression?
✔ Definition:
Logistic Regression is a classification algorithm that
estimates the probability of categorical outcomes using
a mathematical function called the sigmoid function.
, ✔ Core Idea:
Takes input features
Applies a linear equation
Converts result into probability (0 to 1)
✔ Decision Rule:
If probability ≥ 0.5 → Class 1
If probability < 0.5 → Class 0
⭐ Key Points of Logistic Regression
1. Categorical Output
Output is discrete, not continuous
Example: Yes/No, True/False, 0/1
2. Probabilistic Output
Gives values between 0 and 1
Shows likelihood of class membership
3. S-Shaped Curve
Uses sigmoid function
Produces an S-shaped curve
Helps in classification
Linear Model Classification: Logistic Regression
⭐ Introduction
Logistic Regression is a supervised machine learning
algorithm used for classification problems. It predicts
the probability that a given input belongs to a specific
class (0 or 1).
It is widely used in real-world applications like:
Spam detection
Medical diagnosis
Credit approval
📌 What is Logistic Regression?
✔ Definition:
Logistic Regression is a classification algorithm that
estimates the probability of categorical outcomes using
a mathematical function called the sigmoid function.
, ✔ Core Idea:
Takes input features
Applies a linear equation
Converts result into probability (0 to 1)
✔ Decision Rule:
If probability ≥ 0.5 → Class 1
If probability < 0.5 → Class 0
⭐ Key Points of Logistic Regression
1. Categorical Output
Output is discrete, not continuous
Example: Yes/No, True/False, 0/1
2. Probabilistic Output
Gives values between 0 and 1
Shows likelihood of class membership
3. S-Shaped Curve
Uses sigmoid function
Produces an S-shaped curve
Helps in classification
