Welcome to our Support Center
Updated

MeanSquaredError

Description

Computes the mean of squares of errors between labels and predictions.​ 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 Squared Error (MSE) is a loss function commonly used in regression problems. It calculates the average of the squares of the differences between predictions and actual values. This metric is favorable when a greater penalty for larger errors is desired, as larger deviations are amplified by the square of the differences.

MSE is particularly suitable for models where minimizing the impact of large errors is crucial and where prediction accuracy is critical, such as in financial predictions or weather modeling.

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).

Table of Contents