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

The correct answer is D. Apply a linear discriminant analysis. Linear Discriminant Analysis (LDA) is the correct feature engineering strategy for crowd sentiment classification models. LDA is a supervised dimensionality-reduction technique that finds the linear combinations of input features that best separate distinct classes (e.g…

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Question

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

Options

AApply an analysis of variance (ANOVA).
BApply a Pearson correlation coefficient.
CApply a Spearman correlation coefficient.
DApply a linear discriminant analysis.

How the community answered

(47 responses)

A
2% (1)
B
6% (3)
C
13% (6)
D
79% (37)

Explanation

Linear Discriminant Analysis (LDA) is the correct feature engineering strategy for crowd sentiment classification models. LDA is a supervised dimensionality-reduction technique that finds the linear combinations of input features that best separate distinct classes (e.g., positive, negative, neutral sentiment). By maximizing the ratio of between-class variance to within-class variance, LDA produces transformed features that are optimally discriminative for classification. ANOVA (A) is used for statistical significance testing of individual features, not for creating new feature representations. Pearson (B) and Spearman (C) correlation coefficients measure pairwise relationships between variables and are used for feature selection or collinearity analysis, not for generating discriminative feature spaces suited to multi-class sentiment tasks.

Topics

#Feature Engineering#Linear Discriminant Analysis#Dimensionality Reduction#Data Preparation

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