PsiPredict(Model, Input_Data, [Header])

Predicts the response, target, output or dependent variable for Input_Data whether it is continuous (Regression) or categorical (Classification) when the model is stored in PMML format.  In addition, this function also computes the fitted values for a Time Series model when the model is stored in PMML format.   Note:  If using an Excel version that does not support Dynamic Arrays, this formula must be entered as an Excel array. 

Model:  Range containing the stored Classification, Regression or TimeSeries model in PMML format.  

Input_Data:  Range containing the new data for computing predictions. Range must contain a header row with column names and at least one row of data containing the exact same features (or columns) as the data used to create the model.

Input_Data:  (Optional) If True, a heading will be inserted above the result collection    

In Analytic Solver Cloud or in newer versions of desktop Excel, PsiPredict() returns a Dynamic Array (see Note in section heading, above)  To use this function as a dynamic array, you need only enter the Psi function in one cell as a normal function, i.e., not as a control array.  The contents of the Dynamic Array will "spill" down the column.  If a nonblank cell is "blocking" the contents of the Dynamic Array, PsiForecast() will return #SPILL until such time as the blockage is removed. 

Output: A single column containing the header and predicted/fitted values for each record in Input_Data

To know if the result of the prediction is continuous or categorical, you must know what kind of model you are passing as an argument to the scoring function – if you previously fitted the classification model and are now predicting the new feature vectors, you should expect to get the compatible categorical response.  On the other hand, you should expect the continuous response from the new data prediction when using a fitted regression model. In previous versions, the user was expected to know the exact type model, such as Mulitple Linear Regression or Discriminant Analysis, to know what kind of output will be produced, whereas starting with V2017, it is sufficient to know whether you’re pointing to a classification or regression model in order to determine the type of the response.  Note:  If the user intends to use an “unknown” model for scoring, the stored worksheets contain the complete information about the model including several clear indications of the model type and data dictionaries with the types of features and response.

In addition, in starting with V2017, PsiPredict() can compute the fitted values for the new time series based on the provided Time Series model. Unlike future-looking forecasting, provided by PsiForecast(), PsiPredict() computes a model prediction for each observation in the provided new time series.

Supported Models

  • Classification:
    • Linear Discriminant Analysis
    • Logistic Regression
    • K-Nearest Neighbors
    • Classification Tree
    • Naïve Bayes
    • Neural Network
    • Random Trees
    • Bagging (with any supported weak learner)
    • Boosting (with any supported weak learner)
  • Regression:
    • Logistic Regression
    • K-Nearest Neighbors
    • Neural Network
    • Bagging (with any supported weak learner)
    • Boosting (with any supported weak learner)
  • Time Series (fitted values)
    • ARIMA
    • Exponential Smoothing
    • Double Exponential Smoothing
    • Holt-Winters Smoothing

Previous related PSI Scoring Functions:

  • Classification: PsiClassifyLR, PsiClassifyDA, PsiClassifyCT, PsiClassifyNB, PsiClassifyNN, PsiClassifyCTEnsemble, PsiClassifyNNEnsemble
  • Regression: PsiPredictMLR, PsiPredictRT, PsiPredictNN, PsiPredictNNEnsemble, PsiPredictRTEnsemble​​​​​​
Prediction/Classification/Time Series Algorithm Stored Model Sheet
Linear Discriminant Analysis Classification DA_Stored
Logistic Regression Classification LogReg_Stored
k-Nearest Neighbors Classification KNNC_Stored
Classification Trees CT_Stored
Naive Bayes Classification NB_Stored
Neural Networks Classification


Ensemble Methods for Classification




Multiple Linear Regression LinReq_Stored
k-Nearest Neighbors Regression KNNP_Stored
Regression Tree RT_Stored
Neural Network Regression NNP_Stored
Ensemble Methods for Regression




Exponential Smoothing Expo_Stored
Double Exponential Smoothing DoubleExpo_Stored
Moving Average Smooting MovingAvg_Stored
Holt Winters Smoothing