Logistic Regression Code – Telecom Churn Example

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Lets explore logisitic regression code done in python today. We have a dataset available for sample telecom provided where we have data of its customer who may or may not churn.

We have to make a prediction on the data set as accurately as possible.

Lets see how we can do that !

Logistic Regression Interview Questions – Part 3

Q1. What is accuracy?
Accuracy is the number of correct predictions out of all predictions made.

Accuracy=True Positives+True NegativesTotal Number of Predictions

Q2. Why is accuracy not a good measure for classification problems?
Accuracy is not a good measure for classification problems because it gives equal importance to both false positives and false negatives. However, this may not be the case in most business problems. For example, in the case of cancer prediction, declaring cancer as benign is more serious than wrongly informing the patient that he is suffering from cancer. Accuracy gives equal importance to both cases and cannot differentiate between them.

Logistic Regression Interview Questions – Part 2

Q1. What is the Maximum Likelihood Estimator (MLE)?
The MLE chooses those sets of unknown parameters (estimator) that maximise the likelihood function. The method to find the MLE is to use calculus and setting the derivative of the logistic function with respect to an unknown parameter to zero, and solving it will give the MLE. For a binomial model, this will be easy, but for a logistic model, the calculations are complex. Computer programs are used for deriving MLE for logistic models.

(Here’s another approach to answering the question.)