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

Hodson, Timothy O.

doi:10.5194/gmd-2022-64

Cited by 38 publications

(35 citation statements)

References 19 publications

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“…The results obtained by the ANFIS model showed that, when reaching the defined stopping criteria of error tolerance and number of epochs, the RMSE values were 0.199 in the data training, and 1.217 in the data validation. The model performance was satisfactory considering that the RMSE value, the smaller the difference between the estimated and the real values [85,86]. According to the Rule Viewer results (Figure 7), the influence of input variables in determining the output index can be measured.…”

Section: Resultsmentioning

confidence: 93%

Development of Adaptive Neuro-Fuzzy Inference System for Assessing Industry Leadership in Accident Situations

et al. 2022

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Petroleum activity is characterized as a high-risk activity due to the probability of accidents with material and human losses. The leaders of this segment assume, besides the complex routine tasks, the challenge of making assertive decisions during an accident. This study aims to present an evaluation model of the Industry Leadership Index for Emergencies Situations (ILIE), using the Adaptive Neuro-Fuzzy System (ANFIS). The model was composed of 4 input variables, namely: knowledge, behavior, skill, and attitude; and one output variable, Industry Leadership. The data collection took place in petroleum production units in Brazil, with a sample of 151 respondents through the application of a survey. The observed data were treated in an Excel tabulator and used in the development of the ANFIS model. From this model, it was possible to carry out simulations to predict the impact, which the increase or decrease in the value of each input variable can influence the leader's profile. The model performed satisfactorily in the Root of the Mean Square Error (RMSE) analysis, being 0.199 in data training and 1.217 in data verification. The results suggest that the ANFIS method can be successfully applied to establish a model to analyze industry leaders prepared for assertive responses in crisis scenarios. .

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Section: Resultsmentioning

confidence: 93%

Development of Adaptive Neuro-Fuzzy Inference System for Assessing Industry Leadership in Accident Situations

et al. 2022

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“…

is expressed as the ground truth value of time

, and

represents the global consistency position calculated by the acoustic sensor network at this time. The RMSE is defined as [ 32 ]

where

denotes the number of frames. Generally, the smaller the RMSE, the better the tracking result.…”

Section: Experiments and Results Discussionmentioning

confidence: 99%

Acoustic Source Tracking Based on Probabilistic Data Association and Distributed Cubature Kalman Filtering in Acoustic Sensor Networks

Yang

Cao

Wang

2022

Sensors

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A probabilistic data association-based distributed cubature Kalman filter (PDA-DCKF) method is proposed in this paper, whose performance on tracking single moving sound sources in the distributed acoustic sensor network was verified. In this method, the PDA algorithm is first used to sift the observations from neighboring nodes. Then, the sifted observations are fused to update the state vectors in the CKF. Since nodes in a sensor network have different reliabilities, the final tracking result integrates the estimations from the local nodes, which are weighted with the parameters depending on the mean square error of the estimation and the energy of the received signal. The experimental results illustrated that the proposed PDA-DCKF method is superior to the other DCKF methods in tracking sound sources even under severe noise and reverberant conditions.

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“…It determines how near the data is to the best fit line. For regression analysis, forecasting, and climatology, RMSE is commonly utilized [45].…”

Section: Root Mean Square Error (Rmse)mentioning

confidence: 99%

“…It is the difference between the expected and the actual numbers, and it is used to figure out where the forecast went wrong. e MAE is used to anticipate and predict the deep learning classifiers, with the resultant value ranging from 0 to infinite [45].…”

Section: Root Mean Square Error (Rmse)mentioning

confidence: 99%

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Zaidi

Chatterjee

Belhaouari

2022

Applied Computational Intelligence and Soft Computing

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In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people’s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70 : 30 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.

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

Cited by 38 publications

References 19 publications

Development of Adaptive Neuro-Fuzzy Inference System for Assessing Industry Leadership in Accident Situations

Development of Adaptive Neuro-Fuzzy Inference System for Assessing Industry Leadership in Accident Situations

Acoustic Source Tracking Based on Probabilistic Data Association and Distributed Cubature Kalman Filtering in Acoustic Sensor Networks

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

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