Which ONE of the below is NOT likely to cause a data quality issue affecting a single ML model?

The correct answer is C. Incorrect weights. Incorrect weights refer to parameters within a trained model and are not typically considered a data quality issue. Data quality issues usually arise from the input data itself, such as security issues, hardware issues, or faulty sensors, which can lead to incorrect or missing…

Machine Learning (ML)

Question

Options

ASecurity issues
BHardware issues
CIncorrect weights
DFaulty sensors

How the community answered

(37 responses)

A
8% (3)
B
16% (6)
C
73% (27)
D
3% (1)

Explanation

Incorrect weights refer to parameters within a trained model and are not typically considered a data quality issue. Data quality issues usually arise from the input data itself, such as security issues, hardware issues, or faulty sensors, which can lead to incorrect or missing data. Incorrect weights are a problem during the model training or fine-tuning phase, not directly related to data

Topics

#data quality#ML model#model weights#faulty sensors

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