Results 1 to 10 of about 296,712 (177)

Forecast Error Calculation with Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE)

JINAV: Journal of Information and Visualization, 2020
Calculation errors in forecasting a data are very important from a forecasting process. The high level of forecasting accuracy will affect the level of confidence in forecasting decision making.
A. S. Ahmar
openaire   +2 more sources

Decomposition of the mean absolute error (MAE) into systematic and unsystematic components.

PLoS ONE, 2023
When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE).
Scott M Robeson, Cort J Willmott
doaj   +2 more sources

Mean Squared Error, Deconstructed

Journal of Advances in Modeling Earth Systems, 2021
As science becomes increasingly cross‐disciplinary and scientific models become increasingly cross‐coupled, standardized practices of model evaluation are more important than ever.
Timothy O Hodson, Thomas M Over, Sydney S Foks   +2 more
exaly   +2 more sources

On the Value of Information and Mean Squared Error for Noisy Gaussian Models

IEEE Communications Letters, 2022
The relationship between the age of information (AoI) and the mean squared error (MSE) in optimisation problems has been widely investigated for various Gaussian Markov models.
Zijing Wang, Mihai-Alin Badiu, Justin P Coon   +2 more
exaly   +2 more sources

Mean Squared Error (MSE) dan Penggunaannya

Jurnal Pemanfaatan Teknologi untuk Masyarakat: Jurnal Pengabdian Masyarakat
Mean Squared Error (MSE) adalah metrik evaluasi yang umum digunakan dalam statistik dan machine learning untuk mengukur seberapa akurat sebuah model regresi dalam memprediksi nilai numerik. MSE menghitung selisih antara nilai prediksi model dan nilai sebenarnya dari data, kemudian mengkuadratkan selisih tersebut agar tidak ada selisih yang bernilai ...
H. Nuha
openaire   +2 more sources

Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling

Journal of Hydrology, 2009
The mean squared error (MSE) and the related normalization, the Nash-Sutcliffe efficiency (NSE), are the two criteria most widely used for calibration and evaluation of hydrological models with observed data. Here, we present a diagnostically interesting
Hoshin V Gupta, Harald Kling, Koray K Yilmaz   +2 more
exaly   +2 more sources

A Competitive Mean-Squared Error Approach to Beamforming

IEEE Transactions on Signal Processing, 2007
We treat the problem of beamforming for signal estimation where the goal is to estimate a signal amplitude from a set of array observations. Conventional beamforming methods typically aim at maximizing the signal-to-interference-plus-noise ratio (SINR ...
Yonina C Eldar, Arye Nehorai, Patricio S la Rosa   +2 more
exaly   +2 more sources

Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation [PDF]

International Joint Conference on Artificial Intelligence, 2021
Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures.
Taehyeon Kim, Jaehoon Oh, Nakyil Kim, Sangwook Cho, Se-Young Yun   +4 more
semanticscholar   +1 more source

Advancements in Downscaling Global Climate Model Temperature Data in Southeast Asia: A Machine Learning Approach

Forecasting, 2023
Southeast Asia (SEA), known for its diverse climate and broad coastal regions, is particularly vulnerable to the effects of climate change. The purpose of this study is to enhance the spatial resolution of temperature projections over Southeast Asia (SEA)
Teerachai Amnuaylojaroen
doaj   +1 more source

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

PeerJ Computer Science, 2021
Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables.
D. Chicco, M. Warrens, Giuseppe Jurman
semanticscholar   +1 more source