You are solving a classification task. The dataset is imbalanced. You need to select an Azure Machine Learning Studio module to improve the classification accuracy. Which module should you use?

The correct answer is C. Synthetic Minority Oversampling Teachnique (SMOTE). SMOTE (Synthetic Minority Oversampling Technique) directly addresses class imbalance by generating synthetic samples for the minority class, thereby balancing the dataset and improving classification accuracy. Fisher Linear Discriminant Analysis is a dimensionality reduction…

Design and prepare a machine learning solution

Question

You are solving a classification task. The dataset is imbalanced. You need to select an Azure Machine Learning Studio module to improve the classification accuracy. Which module should you use?

Options

AFisher Linear Discriminant Analysis.
BFilter Based Feature Selection
CSynthetic Minority Oversampling Teachnique (SMOTE)
DPermutation Feature Importance

How the community answered

(53 responses)

A
4% (2)
B
13% (7)
C
75% (40)
D
8% (4)

Explanation

SMOTE (Synthetic Minority Oversampling Technique) directly addresses class imbalance by generating synthetic samples for the minority class, thereby balancing the dataset and improving classification accuracy. Fisher Linear Discriminant Analysis is a dimensionality reduction technique, Filter Based Feature Selection removes irrelevant features, and Permutation Feature Importance ranks feature contributions to a model - none of these resolve the core problem of class imbalance that degrades classifier performance on the minority class.

Topics

#Imbalanced data#SMOTE#Classification#Azure Machine Learning

Community Discussion

No community discussion yet for this question.

Full DP-100 Practice