Results 11 to 20 of about 403,368 (289)

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems [PDF]

Frontiers in Plant Science, 2022
Irregular changes in the internal climates of protected cultivation systems can prevent attainment of optimal yield when the environmental conditions are not adequately monitored and controlled.
Daniel Dooyum Uyeh, Daniel Dooyum Uyeh, Daniel Dooyum Uyeh, Olayinka Iyiola, Olayinka Iyiola, Rammohan Mallipeddi, Senorpe Asem-Hiablie, Maryleen Amaizu, Yushin Ha, Yushin Ha, Yushin Ha, Tusan Park, Tusan Park   +12 more
doaj   +2 more sources

Correcting the Bias of the Root Mean Squared Error of Approximation Under Missing Data [PDF]

Methodology, 2021
Missing data are ubiquitous in psychological research. They may come about as an unwanted result of coding or computer error, participants' non-response or absence, or missing values may be intentional, as in planned missing designs.
Cailey E. Fitzgerald, Ryne Estabrook, Daniel P. Martin, Andreas M. Brandmaier, Timo von Oertzen   +4 more
doaj   +4 more sources

Root mean square error (RMSE) or mean absolute error (MAE): when to use them or not

Geoscientific Model Development, 2022
Abstract. The mean absolute error (MAE) and root mean squared error (RMSE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide.
Timothy O Hodson
openaire   +5 more sources

Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance [PDF]

Climate Research, 2005
The relative abilities of 2, dimensioned statistics — the root-mean-square error (RMSE) and the mean absolute error (MAE) — to describe average model-performance error are examined. The RMSE is of special interest because it is widely reported in the climatic and environmental liter- ature; nevertheless, it is an inappropriate and misinterpreted ...
CJ Willmott, K Matsuura
openaire   +3 more sources

Based a machine learning approach to investigate the factors influencing nirmatrelvir/ritonavir exposure in human plasma: a multicenter, observational study [PDF]

Frontiers in Cellular and Infection Microbiology
ObjectivesNirmatrelvir/ritonavir (N/R) is an effective antiviral for treating COVID-19. However, evidence supporting therapeutic drug monitoring (TDM) for N/R remains limited, potentially increasing the risk of adverse reactions and compromising efficacy.
Yue Zhang, Yue Zhang, Xiner Yang, Xiner Yang, Liang Sun, Liang Sun, Runcong Zhang, Runcong Zhang, Weibin Fan, Weibin Fan, Nengming Lin, Nengming Lin, Nengming Lin, Bin Lin, Bin Lin, Bin Lin   +15 more
doaj   +2 more sources

Moments and root-mean-square error of the Bayesian MMSE estimator of classification error in the Gaussian model [PDF]

Pattern Recognition, 2014
The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge,
Zollanvari, Amin, Dougherty, Edward R.
openaire   +6 more sources

Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model

IOP Conference Series: Materials Science and Engineering, 2018
Most existing Collaborative Filtering (CF) algorithms predict a rating as the preference of an active user toward a given item, which is always a decimal fraction. Meanwhile, the actual ratings in most data sets are integers. In this paper, we discuss and demonstrate why rounding can bring different influences to these two metrics; prove that rounding ...
Weijie Wang, Yanmin Lu
openaire   +3 more sources

Interpolatory model reduction of dynamical systems with root mean squared error

IFAC-PapersOnLine
The root mean squared error is an important measure used in a variety of applications such as structural dynamics and acoustics to model averaged deviations from standard behavior. For large-scale systems, simulations of this quantity quickly become computationally prohibitive.
Reiter, Sean, Werner, Steffen W. R.
openaire   +4 more sources

MS-YieldStackNet: multi-source data fusion for wheat yield estimation using a stacked ensemble neural network [PDF]

PeerJ Computer Science
Accurate crop yield prediction is vital for ensuring food security and informing agricultural policy, particularly in wheat-dependent regions like Pakistan where manual estimation methods are labor-intensive and imprecise.
Waqas Ali, Zeeshan Ramzan, Muhammad Shahbaz, Qamar Ul Zaman Bhutta, Muhammad Talha, Mohammed J. AlGhamdi   +5 more
doaj   +3 more sources

Towards Resilient Agriculture to Hostile Climate Change in the Sahel Region: A Case Study of Machine Learning-Based Weather Prediction in Senegal

Agriculture, 2022
To ensure continued food security and economic development in Africa, it is very important to address and adapt to climate change. Excessive dependence on rainfed agricultural production makes Africa more vulnerable to climate change effects.
Chimango Nyasulu, Awa Diattara, Assitan Traore, Abdoulaye Deme, Cheikh Ba   +4 more
doaj   +1 more source