Scatter( name='Predicted', x=fcst['ds'], y=fcst['yhat'], mode='lines', line=dict(color=prediction_color, width=line_width), fillcolor=error_color, fill='tonexty' if uncertainty else 'none' )) # Add upper bound if uncertainty: data.append(go.Scatter(...
\nOverall accuracy of basic plot: Number of voxels predicted with correct class / number of all voxels.\nClass-specific accuracy of detailed plot: (True Positives + True Negatives with respect to \"the specified class\") / number of all voxels.\n\t>> i.e. voxels predicted with...
Scatterplot of measured and predicted yields from different models and trials. The models consistently performed the best for ADV trials followed by the MID and ERL trials. The dashed blue circle indicates the higher error rate Full size image Figure8also highlights that the inconsistencies between ...
The reference PD value p parameterizes the curve, and the software sweeps through the unique predicted PD values observed in a data set. The proportion of actual defaulters are assigned a PD higher than or equal to p is the true positive rate. The proportion of actual nondefaulters that ar...
The reference PD value p parameterizes the curve, and the software sweeps through the unique predicted PD values observed in a data set. The proportion of actual defaulters are assigned a PD higher than or equal to p is the true positive rate. The proportion of actual nondefaulters that ar...
Spatial analysis of yield prediction The spatial pattern of predicted yield for each plots in each field was performed by using the Global Moran's I statistic. Figure 10 shows the prediction map generated from the best performing DenseNet model in one field from each location. Similar ...