Logistic Regression
Models probabilities for classification problems.
Negative = 0, Positive = 1
Use Cases
- Binary Classification
- Customer Churn Prediction … whether a customer stays or leaves
- Medical Diagnosis … predicting presences/absence of a disease
Advantages
- Can include probabilities
- Can be extended for multi-class classification
Disadvantages
- Worse predictive performances than other models
- Difficult interpretation
- Can’t be trained if there is a feature perfectly separating the two classes