Explanation/Reference:
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.
Incorrect Answers:
A: Logistic regression is a well-known method in statistics that is used to predict the probability of an outcome, and is particularly popular for classification tasks. The algorithm predicts the probability of occurrence of an event by fitting data to a logistic function.
B: Boosting means that each tree is dependent on prior trees. The algorithm learns by fitting the residual of the trees that preceded it. Thus, boosting in a decision tree ensemble tends to improve accuracy with some small risk of less coverage.
C: Decision trees are non-parametric models that perform a sequence of simple tests for each instance, traversing a binary tree data structure until a leaf node (decision) is reached.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson- regression