2
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
3
A novel framework for COVID-19 case prediction through piecewise regression in India
4
Count regression models for COVID-19
5
Covid Pandemic Analysis using Regression
6
Regression Analysis of COVID-19 using Machine Learning Algorithms
7
Classifier uncertainty: evidence, potential impact, and probabilistic treatment
8
A Comprehensive Survey of Loss Functions in Machine Learning
9
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
10
From Generalized Linear Models to Neural Networks, and Back
11
Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico
12
Optimum design of chamfer masks using symmetric mean absolute percentage error
13
Benign overfitting in linear regression
14
A coefficient of determination (R2) for generalized linear mixed models
15
Obesity Level Estimation Software based on Decision Trees
17
Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology
18
Coefficient of Determination
19
Using machine learning techniques to generate laboratory diagnostic pathways—a case study
20
Evaluating Performance of Regression Machine Learning Models Using Multiple Error Metrics in Azure Machine Learning Studio
21
Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model
22
A Coefficient of Determination for Generalized Linear Models
23
A new accuracy measure based on bounded relative error for time series forecasting
24
The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded
25
Points of Significance: Regularization
26
Mean Absolute Percentage Error for regression models
27
Points of Significance: Multiple linear regression
28
Using the Mean Absolute Percentage Error for Regression Models
29
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
30
Linear Regression Analysis
31
The Enhanced Liver Fibrosis (ELF) score: normal values, influence factors and proposed cut-off values.
32
Simple Formulae for Bias and Mean Square Error Computation [DSP Tips and Tricks]
33
Using the coefficient of determination R2 to test the significance of multiple linear regression
34
Handbook of Regression Analysis
35
The coefficient of determination: what determines a useful R² statistic?
36
A Better Coefficient of Determination for Genetic Profile Analysis
37
MICE: Multivariate Imputation by Chained Equations in R
38
Analysis of Mean-Square-Error (MSE) for fixed-point FFT units
39
Accuracy in Parameter Estimation for the Root Mean Square Error of Approximation: Sample Size Planning for Narrow Confidence Intervals
40
A Robust Coefficient of Determination for Regression
41
Applicability of the Revised Mean Absolute Percentage Errors (MAPE) Approach to Some Popular Normal and Non-normal Independent Time Series
42
Coefficient of Determination (R2)
43
Introduction to Linear Regression Analysis
44
Introduction to Regression Analysis
45
Linking data to models: data regression
46
Another look at measures of forecast accuracy
47
Multiresponse robust design: Mean square error (MSE) criterion
48
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
49
R Squared, Adjusted R Squared†
50
Mean-Square-Error Calculations for Average Treatment Effects
51
Regression Analysis: A Constructive Critique
52
Visual Acuity as a Function of Zernike Mode and Level of Root Mean Square Error
53
The Coefficient of Determination, R^2
54
Power and Sample Size for the Root Mean Square Error of Approximation Test of not Close Fit in Structural Equation Modeling
55
Regression Analysis by Example
56
Coefficient of determination in nonlinear signal processing
57
The M3-Competition: results, conclusions and implications
58
Coefficients of Determination for Multiple Logistic Regression Analysis
59
Improving the Root Mean Square Error of Approximation for Nonnormal Conditions in Structural Equation Modeling
60
Generalized Coefficient of Determination
61
On the asymmetry of the symmetric MAPE
62
Applied Regression Analysis: Draper/Applied Regression Analysis
63
Gentleman R: R: A language for data analysis and graphics
64
Accuracy measures: theoretical and practical concerns☆
65
Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons
66
A Comment on the Coefficient of Determination for Binary Responses
67
A note on a general definition of the coefficient of determination
68
Mean square error of regression‐based constituent transport estimates
69
Applied Regression Analysis: A Research Tool
70
Understanding Regression Analysis
71
Correlation and the coefficient of determination
72
Some Comments on the Minimum mean Square Error as a Criterion of Estimation.
73
Further Results on the Mean Square Error of Ridge Regression
74
Comparison of the predicted and observed secondary structure of T4 phage lysozyme.
75
On Generalized Coefficient of Determination (多変量統計解析)
76
On the Bias and Mean Square Error of the Ratio Estimator
77
The Coefficient of Determination—Some Limitations
78
Mean Square Error of Prediction as a Criterion for Selecting Variables
79
Robust Estimation of a Location Parameter
80
Correlation and Causation
81
Personal communication (email)
82
An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis
83
The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment
84
The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment
85
Statistical Learning as a Regression Problem
86
Everything is a regression: in search of unifying paradigms in statistics
88
University of California Irvine Machine Learning Repository
90
How to Estimate Forecasting Quality: A System- Motivated Derivation of Symmetric Mean Absolute Percentage Error (SMAPE) and Other Similar Characteristics
91
Errors on percentage errors
92
A Survey of Forecast Error Measures
93
Bayesian inference for additive mixed quantile regression models
96
BOOTSTRAPPING AND OTHER COMBINED
97
The coefficient of determination exposed !
99
Partition of the Coefficient of Determination in Multiple Regression
100
The Coefficient of Determination: Understanding r2 and R2.
101
The coefficient of determination and its adjusted version in linear regression models
102
Factors that influence the value of the coefficient of determination in simple linear and nonlinear regression models
103
A pragmatic view of accuracy measurement in forecasting
104
On robust estimation of the location parameter
105
A Note on the Use of the Coefficient of Determination
106
Applied Regression Analysis
107
When is R squared negative
108
Is R2 useful or dangerous?
