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 !
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.
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.)
Read More…Q1. What is a logistic function? What is the range of values of a logistic function?
The logistic function is as defined below:
f(z)=1(1+e−z)
The values of a logistic function will range from 0 to 1. The values of Z will vary from −∞ to +∞.
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