Which two conditions must the feature set contain?

You are building a regression model tot estimating the number of calls during an event.

You need to determine whether the feature values achieve the conditions to build a Poisson regression model.

Which two conditions must the feature set contain? I ach correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
A . The label data must be a negative value.
B . The label data can be positive or negative,
C . The label data must be a positive value
D . The label data must be non discrete.
E . The data must be whole numbers.

View Answer

Answer: C,E

Explanation:

Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts.

Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:

✑ The response variable has a Poisson distribution.

✑ Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.

✑ A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non-whole numbers.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-

regression