MLS-C01 Question #251: Real Exam Question with Answer & Explanation

Question

A retail company wants to create a system that can predict sales based on the price of an item. A machine learning (ML) engineer built an initial linear model that resulted in the following residual plot: Which actions should the ML engineer take to improve the accuracy of the predictions in the next phase of model building? (Choose three.)

Options

• ADownsample the data uniformly to reduce the amount of data.
• BCreate two different models for different sections of the data.
• CDownsample the data in sections where Price < 50.
• DOffset the input data by a constant value where Price > 50.
• EExamine the input data, and apply non-linear data transformations where appropriate.
• FUse a non-linear model instead of a linear model.

Explanation

To improve the accuracy of a linear model whose residual plot shows a non-random pattern, the ML engineer should address the underlying non-linearity by considering multiple models for different data sections, applying non-linear data transformations, or using a non-linear model entirely.

Common mistakes.

• A. Downsampling data uniformly reduces the available information for training and is unlikely to address issues like non-linearity or heteroscedasticity visible in a residual plot.
• C. Downsampling data only in specific sections reduces valuable information for those areas and does not inherently resolve the model's inability to capture the correct relationship.
• D. Offsetting input data by a constant value is a linear transformation and will not resolve non-linear patterns or heteroscedasticity evident in the residual plot.

Concept tested. Residual analysis, improving model fit, non-linear modeling

Reference. https://scikit-learn.org/stable/modules/linear_model.html

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