You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?

A. Apply an analysis of variance (ANOVA).

B. Apply a Pearson correlation coefficient.

C. Apply a Spearman correlation coefficient.

D. Apply a linear discriminant analysis.








 

Suggested Answer: D

The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data.
All shared features for local models are continuous variables.
Incorrect Answers:
B: The Pearson correlation coefficient, sometimes called Pearson's R test, is a statistical value that measures the linear relationship between two variables. By examining the coefficient values, you can infer something about the strength of the relationship between the two variables, and whether they are positively correlated or negatively correlated.
C: Spearman's correlation coefficient is designed for use with non-parametric and non-normally distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence between two variables, and is sometimes denoted by the Greek letter rho. The Spearman's coefficient expresses the degree to which two variables are monotonically related. It is also called Spearman rank correlation, because it can be used with ordinal variables.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisher-linear-discriminant-analysis
 https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation

This question is in DP-100 Exam
For getting Microsoft Azure Data Scientist Associate Certificate


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