Mean absolute percentage error

Mean Absolute Percentage Error (MAPE) measures the accuracy of fitted time series values. It expresses accuracy as a percentage. 

The method of drying, type of samples, Mean Absolute Deviation, Mean Absolute Percentage Error, Mean Squared Deviation of these models used for moisture content change with time are presented in Table 1. 

Mean absolute percentage error (MAPE) this criterion is less sensitive to large errors than RMSE and can be expressed as... 

Choose an error measure, e.g. RMSE, mean absolute percentage error (MAPE), etc., for assessing the output prediction accuracy of the candidate solutions. 

The performances of different developed networks are presented in Table 5.5. It is seen from the table that increasing the number of neurons in the hidden layer does not always ensure that the mean squared error will decrease but it is clear that by increasing the number of neurons, the architecture gets complicated. The architecture 3-17-1 gives the minimum mean squared error and is selected as the best performing architecture. This network also provides the highest correlation coefficient and minimum mean absolute percentage error. 

In this study, the best prediction, based on minimum error, was obtained by ANN with one hidden layer. The suitable number of neurons in the hidden layer was determined by changing the number of neurons. The good prediction and minimum error value were obtained with four neurons in the hidden layer. The weights and bias of ANN for CA of electrospun fiber mat are given in Table 4. The R and mean absolute percentage error were 0.965 and 5.94% respectively, which indicates that the model was shows good fitting with experimental data. 

The mean absolute percentage error (MAPE) is the average absolute error as a percentage of d and and is given by... 

Once the ANN has been trained, its performance is evaluated separately in the training and testing dada sets. Correlation coefficients (R) between the experimental and predicted values, mean absolute percentage error (MAPE) and mean squared error (MSE) are the statistical parameters with which the performance of ANN is appraised. The expressions of these statistical parameters have been given below ... 

Fig. 4 Left the mean 1961-1990 monthly temperature for the Ebro catchment. Part (a) shows the annual cycle, each line representing a different RCM simulation and the bold line representing the CRU observed series. The shading represents the 95% confidence interval for the estimate of the observed 30-year sample mean. Part (b) represents the individual monthly model means as an anomaly from the CRU mean with 95% confidence interval superimposed. Part (c) represents the mean absolute annual error for each of the RCMs. Right-, as for left column but for mean precipitation (d) for the Gallego catchment. Model anomalies in parts (e) and (f) are expressed as a percentage relative to the CRU monthly mean. Model numbers correspond to experiments shown in Table 1. Figure from [35]...

The MSB values for different architectures are presented in Table 5.21. From the table, it is seen that for mild steel, increase in the number of neurons in the hidden layer beyond four does not improve in the performance and thus the network with 3-4-1 architecture is selected based on minimum mean squared error. For mild steel, the maximum absolute percentage error is obtained as 2.42%, which implies that the ANN model outputs and experimental outputs are very close to each other. The comparative study of experimental and ANN model predicted fractal dimension is presented in Fignre 5.15. From this figure also, it is clear that the predicted and experimental... 

Table 4.3. Comparison of the excitation energies of neutral helimn, calculated from the exact xc potential [49] by using approximate xc kernels. SPA stands for single pole approximations , while fuU means the solution of (4.107) neglecting continuum states. The exact values are from a non-relativistic variational calculation [53]. The mean absolute deviation and mean percentage errors also include the transitions from the Is until the 9s and 9p states. All energies are in hartrees. Table adapted from [17]...

Figure 10.15 plots the comparison of experimental and model generated bubble point pressures for the consolidated database of 5224 data points. Table 10.6 compiles the model performance against all of the data sets, organized by author where d,

data points that lie with 5% and 10% of the model prediction respectively, and the mean absolute error (MAE) is defined as ... 

The relative error of a measurement (or on the mean value) corresponds to the ratio of the absolute value of the deviation corresponding to e, (or e), over the true value. can be expressed as a percentage or in ppm. 

The absolute or mean error expressed as a percentage of the true value is the relative error. The above analysis has a relative error of (—0.10/2.62) X 100% = —3.8%. The relative accuracy is the measured value or mean expressed as a percentage of the true value. The above analysis has a relative accuracy of (2.52/2.62) X 100% = 96.2%. We should emphasize that neither number is known to be true, and the relative error or accuracy is based on the mean of two sets of measurements. 

The goodness of a calibration can be summarized by two values, the percentage of variance explained by the model and the Root Mean Square Error in Calibration (RMSEC). The former, being a normalized value, gives an initial idea about how much of the variance of the data set is captured by the model the latter, being an absolute value to be interpreted in the same way as a standard deviation, gives information about the magnitude of the error. 

The true mass of a glass bead is 0.1026 g. A student takes four measurements of the mass of the bead on an analytical balance and obtains the following results 0.1021 g, 0.1025 g, 0.1019 g, and 0.1023 g. Calculate the mean, the average deviation, the standard deviation, the percentage relative standard deviation, the absolute error of the mean, and the relative error of the mean. 

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