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Question 169 - MLS-C01 discussion

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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?

A.
Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
Answers
A.
Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
B.
Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
Answers
B.
Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
C.
Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
Answers
C.
Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
D.
Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
Answers
D.
Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
Suggested answer: D

Explanation:

Feature selection is the process of reducing the number of input variables to those that are most relevant for predicting the target variable. One way to do this is to run a correlation check of all features against the target variable and remove features with low target variable correlation scores. This means that these features have little or no linear relationship with the target variable and are not useful for the prediction. This can reduce the model's complexity and improve its performance.References:

Feature engineering - Machine Learning Lens

Feature Selection For Machine Learning in Python

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asked 16/09/2024
Arkadius Thoma
46 questions
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