MLS-C01 Question #102: Real Exam Question with Answer & Explanation

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.

Explanation

https://deep-r.medium.com/difference-between-variance-co-variance-and-correlation-

No community discussion yet for this question.