In this post, you will learn about the concepts of the mean-squared error (MSE) and R-squared, the difference between them, and which one to use when evaluating the linear regression models. You also learn Python examples to understand the concepts in a better manner
What is Mean Squared Error (MSE)?
The Mean squared error (MSE) represents the error of the estimator or predictive model created based on the given set of observations in the sample. Intuitively, the MSE is used to measure the quality of the model based on the predictions made on the entire training dataset vis-a-vis the true label/output value. In other words, it can be used to represent the cost associated with the predictions or the loss incurred in the predictions. And, the squared loss (difference between true & predicted value) is advantageous because they exaggerate the difference between the true value and the predicted value. Two or more regression models created using a given sample of data can be compared based on their MSE. The lesser the MSE, the better the regression model is. When the linear regression model is trained using a given set of observations, the model with the least mean sum of squares error (MSE) is selected as the best model. The Python or R packages select the best-fit model as the model with the lowest MSE or lowest RMSE when training the linear regression models.
In 1805, the French mathematician Adrien-Marie Legendre, who first published the sum of squares method for gauging the quality of the model stated that squaring the error before summing all of the errors to find the total loss is convenient. The question that may be asked is why not calculate the error as the absolute value of loss (difference between y and y_hat in the following formula) and sum up all the errors to find the total loss. The absolute value of error is not convenient, because it doesn’t have a continuous derivative, which does not make the function smooth. And, the functions that are not smooth are difficult to work with when trying to find closed-form solutions to the optimization problems by employing linear algebra concepts.
Mathematically, the MSE can be calculated as the average sum of the squared difference between the actual value and the predicted or estimated value represented by the regression model (line or plane). It is also termed as mean squared deviation (MSD). This is how it is represented mathematically:
The value of MSE is always positive. A value close to zero will represent better quality of the estimator/predictor (regression model).
An MSE of zero (0) represents the fact that the predictor is a perfect predictor.
When you take a square root of MSE value, it becomes root mean squared error (RMSE). RMSE has also been termed root mean square deviation (RMSD). In the above equation, Y represents the actual value and the Y_hat represents the predicted value that could be found on the regression line or plane. Here is the diagrammatic representation of MSE for a simple linear or univariate regression model:
What is R-Squared?
R-Squared is the ratio of the sum of squares regression (SSR) and the sum of squares total (SST). Sum of Squares Regression (SSR) represents the total variation of all the predicted values found on the regression line or plane from the mean value of all the values of response variables. The sum of squares total (SST) represents the total variation of actual values from the mean value of all the values of response variables. R-squared value is used to measure the goodness of fit or best-fit line. The greater the value of R-Squared, the better is the regression model as most of the variation of actual values from the mean value get explained by the regression model. However, we need to take caution while relying on R-squared to assess the performance of the regression model. This is where the adjusted R-squared concept comes into the picture. This would be discussed in one of the later posts. R-Squared is also termed as the coefficient of determination. For the training dataset, the value of R-squared is bounded between 0 and 1, but it can become negative for the test dataset if the SSE is greater than SST. Greater the value of R-squared would also mean a smaller value of MSE. If the value of R-Squared becomes 1 (ideal world scenario), the model fits the data perfectly with a corresponding MSE = 0. As the value of R-squared increases and become close to 1, the value of MSE becomes close to 0.
Here is a visual representation to understand the concepts of R-Squared in a better manner.
Pay attention to the diagram and note that the greater the value of SSR, the more is the variance covered by the regression / best fit line out of total variance (SST). R-Squared can also be represented using the following formula:
R-Squared = 1 – (SSE/SST)
Pay attention to the diagram and note that the smaller the value of SSE, the smaller is the value of (SSE/SST), and hence greater will be the value of R-Squared. Read further details on R-squared in this blog – R-squared/R2 in linear regression: Concepts, Examples
R-Squared can also be expressed as a function of mean squared error (MSE). The following equation represents the same. You may notice that as MSE increases, the value of R2 will decrease owing to the fact that the ratio of MSE and Var(y) will increase resulting in the decrease in the value of R2.
Difference between Mean Square Error & R-Squared
The similarity between mean-squared error and R-Squared is that they both are a type of metrics that are used for evaluating the performance of the linear regression models.
The difference is that MSE gets pronounced based on whether the data is scaled or not. For example, if the response variable is housing price in the multiple of 10K, MSE will be different (lower) than when the response variable such as housing pricing is not scaled (actual values). This is where R-Squared comes to the rescue. R-Squared is also termed the standardized version of MSE. R-squared represents the fraction of variance of the actual value of the response variable captured by the regression model rather than the MSE which captures the residual error.
MSE or R-Squared – Which one to Use?
It is recommended to use R-Squared or rather adjusted R-Squared for evaluating the model performance of the regression models. This is primarily because R-Squared captures the fraction of variance of actual values captured by the regression model and tends to give a better picture of the quality of the regression model. Also, MSE values differ based on whether the values of the response variable are scaled or not. A better measure instead of MSE is the root mean squared error (RMSE) which takes care of the fact related to whether the values of the response variable are scaled or not.
One can alternatively use MSE or R-Squared based on what is appropriate and the need of the hour. However, the disadvantage of using MSE than R-squared is that it will be difficult to gauge the performance of the model using MSE as the value of MSE can vary from 0 to any larger number. However, in the case of R-squared, the value is bounded between 0 and 1. A value of R-squared closer to 1 would mean that the regression model covers most part of the variance of the values of the response variable and can be termed as a good model. However, with the MSE value, depending on the scale of values of the response variable, the value will be different and hence, it would be difficult to assess for certain whether the regression model is good or otherwise.
MSE or R-Squared Python Code Example
Here is the python code representing how to calculate mean squared error or R-Squared value while working with regression models. Pay attention to some of the following in the code given below:
- Sklearn.metrics mean_squared_error and r2_score is used for measuring the MSE and R-Squared values. Input to this methods are actual values and predicted values.
- Sklearn Boston housing dataset is used for training a multiple linear regression model using Sklearn.linear_model LinearRegression
import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression from sklearn.pipeline import make_pipeline from sklearn.metrics import mean_squared_error, r2_score from sklearn import datasets # # Load the Sklearn Boston Dataset # boston_ds = datasets.load_boston() X = boston_ds.data y = boston_ds.target # # Create a training and test split # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # # Fit a pipeline using Training dataset and related labels # pipeline = make_pipeline(StandardScaler(), LinearRegression()) pipeline.fit(X_train, y_train) # # Calculate the predicted value for training and test dataset # y_train_pred = pipeline.predict(X_train) y_test_pred = pipeline.predict(X_test) # # Mean Squared Error # print('MSE train: %.3f, test: %.3f' % (mean_squared_error(y_train, y_train_pred), mean_squared_error(y_test, y_test_pred))) # # R-Squared # print('R^2 train: %.3f, test: %.3f' % (r2_score(y_train, y_train_pred), r2_score(y_test, y_test_pred)))
Here is the summary of what you learned in this post regarding mean square error (MSE) and R-Squared and which one to use?
- MSE represents the residual error which is nothing but sum of squared difference between actual values and the predicted / estimated values divided by total number of records.
- R-Squared represents the fraction of variance captured by the regression model.
- The disadvantage of using MSE is that the value of MSE varies based on whether the values of response variable is scaled or not. If scaled, MSE will be lower than the unscaled values.