Name derived from the Logit Transformation
Differences from OLS
- Customer will pay/default on loan (credit Risk)Differences from OLS
- Used for predicting the outcome of a binary dependent variable (yes or no)
- i.e., the DV has to be a Nominal variable restricted to only 2 states
- Customer will respond/ignore the offer (Marketing Response)
- Customer will churn/stay loyal (Telecom etc.)
- Uses a Logit transformation on the DV to fit a linear regression model.
- We can study how probability of passing changes as per the hours studies using joint Probability distribution.
- Can we train a regression model on this relationship.
Logistic Regression - Concepts
- Model the PROBABILITY of an event-rather than a measure
- Need to create a dependent variables as a probability range, requires a transformation from the binary nominal variable in dataset.
- LOGIT transformation used to create the dependent variable, hence the name Logistic Regression.
- All assumptions of OLS regression are still valid, however deviations are tolerated to a large extent - as end result in most cases require only a rank order.
Logistic Regression produces results in a binary format which is used to predict the outcome of a categorical dependent variable. So the outcome should be discrete/categorical such as:
Logistic Regression Curve
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