What is better ? A low R squared or a high R squared?
Question
TheDataMonk
Follow Me
About TheDataMonkGrand Master
I am the Co-Founder of The Data Monk. I have a total of 6+ years of analytics experience 3+ years at Mu Sigma 2 years at OYO 1 year and counting at The Data Monk I am an active trader and a logically sarcastic idiot :)Follow Me
Answers ( 6 )
High R squared
R^2 is the amount of variance explained by the predictor variables that is present
in the target variable. So, the higher the amount of variance the predictors are able to explain,
the better is your model.
So, higher R^2 is always better. The value of R^2 ranges from 0 to 1. so, the closer
the value is to 1 , the better is your model.
R-squared is a metric used in Regression to validate how our model is performing.
It explains to what extent the variance of one variable explains the variance of another variable. So, if the R-squared of the model is 0.50 then approximately half of the observed variance is explained by the model’s input.
If the R-squared value is 0.95 then approximately 95% of the observed variance is explained by the model’s input. If the value of R-squared value is 0.30 then approximately 30% of the observed variance is explained by the model’s input.
So we can tell that higher the R-squared value or close to 1, the better is our model.
R-squared is the percentage of the dependent variable variation that a linear model explains.
R-squared is always between 0 and 100%:
0% represents a model that does not explain any of the variation in the response variable around its mean. The mean of the dependent variable predicts the dependent variable as well as the regression model.
100% represents a model that explains all of the variation in the response variable around its mean.
Usually, the larger the R2, the better the regression model fits your observations
R-squared is a goodness-of-fit measure for linear regression models. This statistic indicates the percentage of the variance in the dependent variable that the independent variables explain collectively. It evaluates the scatter of the data points around the fitted regression line. It is also called the coefficient of determination, or the coefficient of multiple determination for multiple regression. For the same data set, higher R-squared values represent smaller differences between the observed data and the fitted values.
1) 0% represents a model that does not explain any of the variations in the response variable around its mean. The mean of the dependent variable predicts the dependent variable as well as the regression model.
2) 100% represents a model that explains all of the variations in the response variable around its mean.
Usually, the larger the R2, the better the regression model fits your observations.
If all assumptions of the models are verified, yes.
The R-squared value is the amount of variance explained by your model. It is a measure of how well your model fits your data. As a matter of fact, the higher it is, the better is your model.
However, it only applies when the assumptions of the models are fulfilled (e.g. for a linear regression : homogeneity and normality of the data, independence of the variables etc).