The following outputs can be generated by the Partial Least Squares tool. Depending on your configuration and analysis requirements, you can select one or more outputs from the Outputs section.
|
Ouptut |
Type |
Description |
|---|---|---|
|
Predicted Target |
Signal |
The value predicted by the Partial Least Squares model for the target variable based on the input signals. |
|
Coefficients |
Scalar |
Shows the influence of each input signal on the predicted target. Larger coefficient magnitudes indicate a stronger impact on the prediction. |
|
Intercept |
Scalar |
The baseline prediction value used by the model before the influence of the input signals is applied. |
|
Model input properties |
Scalar |
Provides information about the model inputs used during training. This output can be used for model validation and troubleshooting. |
|
R squared |
Scalar |
Indicates how well the model fits the training data. Values closer to 1 indicate that the model explains a larger portion of the variation in the target variable. |
|
Training window |
Condition |
Identifies the time range used to train the Partial Least Squares model. This output can be used to verify the training period and compare it with scored data. |
|
X loadings |
Scalar |
Shows how the input signals contribute to the latent variables identified by the model. Useful for understanding relationships between input signals. |
|
X weights |
Scalar |
Shows the importance of each input signal when constructing the latent variables used by the model. |
|
Y loadings |
Scalar |
Shows how the target variable relates to the latent variables identified by the model. |
|
Y weights |
Scalar |
Shows the contribution of the target variable to the latent variables used by the model. |
|
|
|
The mathematical attributes of X-Loadings, X-Weights, Y-Loadings, and Y-Weights are explained here. |