Explain coefficient of determination in a linear regression model

Question

Ranjana Admin · Accepted Answer

Coefficient of determination or R2 or also goodness of fit is the key output used in regression analysis. It can tell the proportion of variance in target variables predicted from the features or attributes.

It is the square of the correlation between actual and predicted output in a regression model.it always ranges between 0 and 1.

If it is greater than 0.8,it indicates that the model is a good model

If it ranges between 0.6 and 0.8,it indicates that the model is a moderate model

If it is less than 0.6,it indicates that the model is a weak model

It is illustrated as below

Mi Bridges · Answer

The coefficient of determination (R²) is a crucial metric in regression analysis, measuring how well the model explains the variance in the target variable. MI Bridges

Key takeaways:

R² = 1 means a perfect fit.

R² > 0.8 indicates a strong model.

0.6 ≤ R² ≤ 0.8 suggests a moderate fit.

R² < 0>

It's a useful measure, but it should be interpreted alongside other metrics like RMSE and residual analysis to fully assess model performance.

Explain coefficient of determination in a linear regression model

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