Binary Classification
Binary Classification
Logistic Regression
Logistic Regression
less than a minute
This course includes
5 articles
9 mins reading time
5 videos • 1 hr 22 mins
Course content
5 sections • 36 topics • Read: 9 mins • Video: 1 hr 22 mins
Binary Classification
Binary Classification
Goal
Decision Boundary
Distance of Points from Separating Hyperplane
Missing Link
Sigmoid Function (a.k.a Logistic Function)
Why is it called Logistic Regression ?
Binary Classification | Sigmoid Function | Why is Logistic Regression called so ?
Log Loss
Log Loss
Cost Function
Gradient Descent
Gradient Calculation
Cost Function Derivative
Prediction Derivative
Sigmoid Derivative
Distance Derivative
Gradient Calculation (combined)
Cost Function Derivative
Gradient Descent (update)
Why MSE can NOT be used as Loss Function?
Log Loss | Logistic Regression | Derivative Calculation | Sigmoid Function Derivative | Explained
Regularization
Why is it a problem ?
Solution
L1 Regularization
L2 Regularization
Regularization in Logistic Regression | L1 & L2 Regularization | Explained
Log Odds
What is the meaning of Odds ?
Log Odds (Logit) Assumption
Log Odds (Logit)
Sigmoid Function
Range of Values
Log Odds (Logit) | Relation to Sigmoid Function | Logistic Regression Logit Assumption | Explained
Probabilistic Interpretation
Why do we use Log Loss in Binary Classification?
Bernoulli Assumption
Maximum Likelihood Estimate (MLE)
Likelihood Function
Problem
Solution
Log Likelihood Function
Inference
Probabilistic Interpretation of Logistic Regression | Bernoulli Assumption | MLE | Explained
Log Loss
Log Loss
Regularization
Regularization in Logistic Regression
Log Odds
Log Odds
Probabilistic Interpretation
Probabilistic Interpretation of Logistic Regression