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Logistic Regression in Machine Learning

Name derived from the Logit Transformation 

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 pay/default on loan (credit Risk)
           - 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.
Example - Hours Studied to passing


- 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
            - Probabilities range from 0 to 1 (Min probability of any event is 0, max is 1)
            - 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