Discover how R-squared measures the accuracy of linear regression models. Learn its significance in assessing model fit and improving data predictions.
Table of Contents
Question
Which of the following metrics can be used to assess how well a linear regression model fits labeled data?
A. Cluster size
B. Number of features
C. R-squared
D. Silhouette coefficient
Answer
C. R-squared
Explanation
The metric you can use to assess how well a linear regression model fits labeled data is R2 or R-squared. This is also referred to as the coefficient of determination. This metric represents the proportion of variance in the dependent variable (label) that can be explained by the independent variables (features) in the model. Its value ranges from 0 to 1, where:
- 0: The model explains none of the variance in the label.
- 1: The model explains all of the variance in the label (perfect fit).
The silhouette coefficient does not assess the model’s fit to the data. This metric is used in clustering algorithms to assess the quality of clusters by measuring the average distance between points within a cluster compared to the distance to points in neighboring clusters. It is not relevant for evaluating a linear regression model fit.
Cluster size does not assess the model’s fit to the data. This metric is relevant in clustering algorithms which are used for grouping data points based on similarity. It is not directly applicable to evaluating linear regression models.
The number of features does not directly assess the model’s fit to the data. This simply indicates the number of independent variables used in the model.
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