Logistic Regression in Machine Learning

Logistic Regression is a supervised machine learning algorithm used for classification problems. Instead of predicting numbers (like Linear Regression), it predicts categories such as: Yes / No, Spam / Not Spam, Pass / Fail etc.
It tells us the probability of something happening, and then we decide the class based on that probability.

How it Works

• The model takes the input features (like study hours, age, income, etc.).
• It calculates a score and then converts that score into a probability between 0 and 1 using thesigmoid function.
• This probability tells us how likely the input belongs to a particular class.
• Decision rule:
  • If probability > 0.5 → Predict Class = 1 (e.g., Pass).
  • If probability ≤ 0.5 → Predict Class = 0 (e.g., Fail).