Algorithms and models

Question 1

regression model

Answer 1

xxx

Question 2

Poisson regression model

Answer 2

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.

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

Question 3

multiple linear regression

Answer 3

xxx

Question 4

linear regression model.

Answer 4

xxx

Question 5

decision tree

Answer 5

xxxq

Question 6

Baysian Linear Regression
Neural Network Regression
Boosted Decision Tree Regression
Linear Regression
Decision Forest Regression

Answer 6

xxx

Question 7

A. Multilayer Perceptions (MLPs)
B. Convolutional Neural Networks (CNNs)
C. Recurrent Neural Networks (RNNs)
D. Generative Adversarial Networks (GANs)

Answer 7

RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both. They’re called recurrent because the network’s hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory.
It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.

Question 8

logistic regression

Answer 8

xxx