MLS-C01 · Question #102
A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as…
The correct answer is D. Run a correlation check of all features against the target variable. To remove features irrelevant to the prediction target (sale price), a machine learning specialist should run a correlation check of all features against the target variable. Features with low or near-zero correlation to the sale price contribute little predictive signal and…
Question
A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi- variable linear regression to predict house sale prices. Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?
Options
- APlot a histogram of the features and compute their standard deviation.
- BPlot a histogram of the features and compute their standard deviation.
- CBuild a heatmap showing the correlation of the dataset against itself.
- DRun a correlation check of all features against the target variable.
How the community answered
(27 responses)- A4% (1)
- C4% (1)
- D93% (25)
Explanation
To remove features irrelevant to the prediction target (sale price), a machine learning specialist should run a correlation check of all features against the target variable. Features with low or near-zero correlation to the sale price contribute little predictive signal and add noise and complexity. This directly answers the question of relevance. A heatmap of features against themselves (option C) shows multicollinearity between features, which is useful for a different problem - avoiding redundant features - but does not directly measure relevance to the target. Plotting histograms and computing standard deviation (options A and B, which are identical) describes the distribution of each feature but gives no information about their relationship to the target variable.
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