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MeanAbsolutePercentageError

Description

Computes the mean absolute percentage error between y_true and y_pred.​ Type : polymorphic.

 

 

 

Input parameters

 

Parameters : cluster,

reduction : enum, type of reduction to apply to the loss. In almost all cases this should be “Sum over Batch“.
sample weights : boolean, if enabled, adds an input for weighting each sample individually.

 

Output parameters

 

Loss : cluster, this cluster defines the loss function used for model training.

enum : enum, an enumeration indicating the loss type (e.g., MSE, CrossEntropy, etc.). If enum is set to CustomLoss, the custom class on the right will be used as the loss function. Otherwise, the selected loss will be applied with its default configuration.
 Class : object, a custom loss class instance.

Required data

 y_pred : array, predicted values.
 y_true : array, true values.

Use cases

Mean Absolute Percentage Error (MAPE) is a loss function used in regression problems to measure a model’s performance in terms of proportional accuracy. It calculates the average of the absolute errors divided by the actual values, expressed as a percentage.

MAPE is particularly useful in contexts where relative errors are more significant than absolute errors, such as in financial planning or sales forecasting, where percentage deviations from actual values are critical for decision-making.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).

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