The NeuralNet_3L class supports multi-output, 3-layer (input, hidden and output) Neural-Networks. It can be used for both classification and prediction, depending on the activation functions used. Given several input vectors and output vectors (training data), fit the parameters a and b connecting the layers, so that for a new input vector v, the net can predict the output value, i.e., yp = f1 (b * f (a * v)) where f and f1 are the activation functions and the parameter a and b are the parameters between input-hidden and hidden-output layers. Unlike NeuralNet_2L which adds input x0 = 1 to account for the intercept/bias, NeuralNet_3L explicitly adds bias.
Value parameters
- f
-
the activation function family for layers 1->2 (input to output)
- f1
-
the activation function family for layers 2->3 (hidden to output)
- fname_
-
the feature/variable names (if null, use x_j's)
- hparam
-
the hyper-parameters for the model/network
- itran
-
the inverse transformation function returns response matrix to original scale
- nz
-
the number of nodes in hidden layer (-1 => use default formula)
- x
-
the m-by-n input/data matrix (training data consisting of m input vectors)
- y
-
the m-by-ny output/response matrix (training data consisting of m output vectors)
Attributes
- Companion
- object
- Graph
-
- Supertypes
-
trait Fittrait FitMtrait PredictorMVtrait FeatureSelectiontrait Modelclass Objecttrait Matchableclass AnyShow all
Members list
Value members
Concrete methods
Build a sub-model that is restricted to the given columns of the data matrix.
Build a sub-model that is restricted to the given columns of the data matrix.
Value parameters
- fname2
-
the variable/feature names for the new model (defaults to null)
- x_cols
-
the columns that the new model is restricted to
Attributes
Make plots for each output/response variable (column of matrix y). Overriden as the response matrix may be transformed or rescaled.
Make plots for each output/response variable (column of matrix y). Overriden as the response matrix may be transformed or rescaled.
Value parameters
- yp
-
the testing/full predicted response/output matrix (defaults to full y)
- yy_
-
the testing/full actual response/output matrix (defaults to full y)
Attributes
- Definition Classes
Given a new input vector v, predict the output/response vector f(v).
Given a new input vector v, predict the output/response vector f(v).
Value parameters
- v
-
the new input vector
Attributes
Given an input matrix v, predict the output/response matrix f(v).
Given an input matrix v, predict the output/response matrix f(v).
Value parameters
- v
-
the input matrix
Attributes
- Definition Classes
Produce a QoF summary for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). FIX - only known to be valid for id activation function
Produce a QoF summary for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). FIX - only known to be valid for id activation function
Value parameters
- b_
-
the parameters/coefficients for the model
- fname_
-
the array of feature/variable names
- x_
-
the testing/full data/input matrix
Attributes
- See also
Test a predictive model y_ = f(x_) + e and return its QoF vector. Testing may be be in-sample (on the training set) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train before test.
Test a predictive model y_ = f(x_) + e and return its QoF vector. Testing may be be in-sample (on the training set) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train before test.
Value parameters
- x_
-
the testing/full data/input matrix (defaults to full x)
- y_
-
the testing/full response/output matrix (defaults to full y)
Attributes
Given training data x_ and y_, fit the parameters bb. Minimize the error in the prediction by adjusting the parameters bb.
Given training data x_ and y_, fit the parameters bb. Minimize the error in the prediction by adjusting the parameters bb.
Value parameters
- x_
-
the training/full data/input matrix
- y_
-
the training/full response/output matrix
Attributes
Given training data x_ and y_, fit the parameters bb. Minimize the error in the prediction by adjusting the parameters bb. This version preforms an interval search for the best eta value.
Given training data x_ and y_, fit the parameters bb. Minimize the error in the prediction by adjusting the parameters bb. This version preforms an interval search for the best eta value.
Value parameters
- x_
-
the training/full data/input matrix
- y_
-
the training/full response/output matrix
Attributes
- Definition Classes
Inherited methods
Make the PREDICTION INTERVAL (PI) lower and upper bound vectors from the point predictions and the interval half widths.
Make the PREDICTION INTERVAL (PI) lower and upper bound vectors from the point predictions and the interval half widths.
Value parameters
- ihw
-
the vector of interval half widths (one for each prediction)
- yp
-
the vector of point predictions (y-hat)
Attributes
- Inherited from:
- Fit
Perform backward elimination to find the least predictive variable to remove from the existing model, returning the variable to eliminate, the new parameter matrix and the new Quality of Fit (QoF). May be called repeatedly.
Perform backward elimination to find the least predictive variable to remove from the existing model, returning the variable to eliminate, the new parameter matrix and the new Quality of Fit (QoF). May be called repeatedly.
Value parameters
- cols
-
the columns of matrix x currently included in the existing model
- first
-
first variable to consider for elimination (default (1) assume intercept x_0 will be in any model)
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures. - Inherited from:
- PredictorMV
Perform BACKWARD ELIMINATION to find the LEAST predictive variables to remove from the full model, returning the variables left and the new Quality of Fit (QoF) measures for all steps.
Perform BACKWARD ELIMINATION to find the LEAST predictive variables to remove from the full model, returning the variables left and the new Quality of Fit (QoF) measures for all steps.
Value parameters
- cross
-
indicator to include the cross-validation/validation QoF measure (defaults to "many")
- first
-
first variable to consider for elimination
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures. - Inherited from:
- PredictorMV
Perform BEAM SEARCH SELECTION to find a GOOD COMBINATION of predictive features/variables to have in the model, returning the top k sets of features/variables selected and the new Quality of Fit (QoF) measures/metrics for all steps. At each step, iterate over the models in the beam (top k) and create candidates by adding features (phase 1) and then removing (phase 2). From all the candidates, keep the best k and start a new iteration. Stops when there is no improvement in any of top k (or the maximum number of features is reached.
Perform BEAM SEARCH SELECTION to find a GOOD COMBINATION of predictive features/variables to have in the model, returning the top k sets of features/variables selected and the new Quality of Fit (QoF) measures/metrics for all steps. At each step, iterate over the models in the beam (top k) and create candidates by adding features (phase 1) and then removing (phase 2). From all the candidates, keep the best k and start a new iteration. Stops when there is no improvement in any of top k (or the maximum number of features is reached.
Value parameters
- bk
-
the beam width holding the top k models (defaults to 3)
- cross
-
indicator to include the cross-validation/validation QoF measure (defaults to "many")
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures/metrics. - Inherited from:
- PredictorMV
Convert QoF results into an array (of size 1) of Statistic for compatibility with the crossValidate method.
Convert QoF results into an array (of size 1) of Statistic for compatibility with the crossValidate method.
Value parameters
- qof
-
the Quality of Fit (QoF) results def qof2Stat (qof: MatrixD): Array [Statistic] = val stats = Fit.qofStatTable // create table for QoF measures if qof(QoF.sst.ordinal)(0) > 0.0 then // requires variation in test-set for q <- qof.indices do stats(q).tally (qof(q)(0)) // tally these QoF measures stats end qof2Stat
Attributes
- Inherited from:
- PredictorMV
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.
Value parameters
- w
-
the weights on the instances (defaults to null)
- y_raw
-
the actual response/output vector to use (test/full)
- yp_raw
-
the predicted response/output vector (test/full)
Attributes
- See also
-
Regression_WLS - Definition Classes
- Inherited from:
- Fit
Diagnose the health of the model by computing the Quality of Fit (QoF) metrics/measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. This method also includes PREDICTION INTERVAL (PI) metrics/measures.
Diagnose the health of the model by computing the Quality of Fit (QoF) metrics/measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. This method also includes PREDICTION INTERVAL (PI) metrics/measures.
Value parameters
- low_up
-
the predicted (lower, upper) bounds vectors
- w
-
the weights on the instances (defaults to null)
- y
-
the actual response/output vector to use (test/full)
- yp
-
the point prediction mean/median
- α
-
the significance/nominal level of uncertainty (α) (defaults to 0.1, 10%)
Attributes
- See also
-
otexts.com/fpp2/prediction-intervals.html Note:
wisshould be computed separately as the bounds are matrices. - Inherited from:
- Fit
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the predicted & actual matrix responses (output variable per column). For some models the instances may be weighted.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the predicted & actual matrix responses (output variable per column). For some models the instances may be weighted.
Value parameters
- w
-
the weights on the instances (defaults to null)
- yy
-
the actual response/output matrix to use (test/full)
- yyp
-
the predicted response/output matrix (test/full)
Attributes
- See also
-
Regression_WLS - Inherited from:
- Fit
Diagnose the health of the model by computing the Quality of Fit (QoF) measures for both POINT PREDICTIONS and PREDICTION INTERVALS.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures for both POINT PREDICTIONS and PREDICTION INTERVALS.
Value parameters
- iα
-
the index for the main significance level out of the vector α
- low_up
-
the predicted (lower, upper) bounds matrices for various α levels (column for each α level)
- y
-
the given time-series (must be aligned with the interval forecast)
- yp
-
the point prediction mean/median
- α
-
the vector of significance levels (defaults to the K = 11 prediction intervals used by the CDC Forecast Hub)
Attributes
- Inherited from:
- Fit
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, specifically for the weighted interval score that allows using custom α levels.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, specifically for the weighted interval score that allows using custom α levels.
Value parameters
- low_up
-
the predicted (lower, upper) bounds matrices for various α levels (column for each α level)
- y
-
the given time-series (must be aligned with the interval forecast)
- yp
-
the point prediction mean/median
- α
-
the vector of significance levels (defaults to the K = 11 prediction intervals used by the CDC Forecast Hub)
Attributes
- Inherited from:
- Fit
Print the model prediction equation in readable form. Override per model.
Print the model prediction equation in readable form. Override per model.
Attributes
- Inherited from:
- Model
Print the model prediction equation in LaTex form. Override per model.
Print the model prediction equation in LaTex form. Override per model.
Attributes
- Inherited from:
- Model
Return the Quality of Fit (QoF) measures corresponding to the labels given. Note, if sse > sst, the model introduces errors and the rSq may be negative, otherwise, R^2 (rSq) ranges from 0 (weak) to 1 (strong). Override to add more quality of fit measures.
Return the Quality of Fit (QoF) measures corresponding to the labels given. Note, if sse > sst, the model introduces errors and the rSq may be negative, otherwise, R^2 (rSq) ranges from 0 (weak) to 1 (strong). Override to add more quality of fit measures.
Attributes
- Definition Classes
- Inherited from:
- Fit
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly.
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly.
Value parameters
- cols
-
the columns of matrix x currently included in the existing model
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures. - Inherited from:
- PredictorMV
Perform FORWARD SELECTION to find the MOST predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.
Perform FORWARD SELECTION to find the MOST predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.
Value parameters
- cross
-
indicator to include the cross-validation/validation QoF measure (defaults to "many")
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures. - Inherited from:
- PredictorMV
Return the best model found from feature selection.
Return the feature/variable names.
Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_.
Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_.
Attributes
- Inherited from:
- PredictorMV
Return the data matrix x concatenated with response vector y.
Return the used response vector y (first colum in matrix).
Return the y-transformation.
Return the used response matrix y. Mainly for derived classes where y is transformed.
Return the used response matrix y. Mainly for derived classes where y is transformed.
Attributes
- Definition Classes
-
PredictorMV -> Model
- Inherited from:
- PredictorMV
Return the help string that describes the Quality of Fit (QoF) measures provided by the Fit trait. Override to correspond to fitLabel.
Return the hyper-parameters.
Perform In-Sample Testing, i.e., train and test on the full data set. Return the prediction and the Quality of Fit.
Perform In-Sample Testing, i.e., train and test on the full data set. Return the prediction and the Quality of Fit.
Value parameters
- showYp
-
whether to show the prediction vector
- skip
-
the number of initial data points to skip (due to insufficient information)
Attributes
- Inherited from:
- PredictorMV
The log-likelihood function times -2. Override as needed.
The log-likelihood function times -2. Override as needed.
Value parameters
- ms
-
raw Mean Squared Error
- s2
-
MLE estimate of the population variance of the residuals
Attributes
- See also
- Inherited from:
- Fit
Return the set of columns (numbers) for the features in this model.
Return the set of columns (numbers) for the features in this model.
Attributes
- Inherited from:
- PredictorMV
Get the model name.
Return the mean of the squares for error (sse / df). Must call diagnose first.
Return the mean of the squares for error (sse / df). Must call diagnose first.
Attributes
- Inherited from:
- Fit
Return the number of terms/parameters in the model, e.g., b_0 + b_1 x_1 + b_2 x_2 has three terms.
Return the number of terms/parameters in the model, e.g., b_0 + b_1 x_1 + b_2 x_2 has three terms.
Attributes
- Inherited from:
- PredictorMV
Order vectors y_ and yp_ accroding to the ascending order of y_.
Order vectors y_ and yp_ accroding to the ascending order of y_.
Value parameters
- y_
-
the vector to order by (e.g., true response values)
- yp_
-
the vector to be order by y_ (e.g., predicted response values)
Attributes
- Inherited from:
- PredictorMV
Return only the first matrix of parameter/coefficient values.
Return only the first matrix of parameter/coefficient values.
Attributes
- Inherited from:
- PredictorMV
Return the array of network parameters (weight matrix, bias vector) bb.
Return the array of network parameters (weight matrix, bias vector) bb.
Attributes
- Inherited from:
- PredictorMV
Produce a PREDICTION INTERVAL half width for each prediction yp (y-hat).
Produce a PREDICTION INTERVAL half width for each prediction yp (y-hat).
Value parameters
- df_
-
the error/residual degrees of freedom
- x_
-
the testing/full data/input matrix
- α
-
the significance level α = .1 for TWO TAILS: left tail .05 | 1 - α = .90 | .05 right tail e.g., for AutoMPG, t_crit (385, 0.90) = 1.6488210657096942 t_crit (385, 0.95) = 1.966
Attributes
- See also
-
predictCIntinPredictorstats.stackexchange.com/questions/585660/what-is-the-formula-for-prediction-interval-in-multivariate-case
- Inherited from:
- Fit
Produce a PREDICTION INTERVAL half width for each prediction yp (y-hat) and each significance level.
Produce a PREDICTION INTERVAL half width for each prediction yp (y-hat) and each significance level.
Value parameters
- df_
-
the error/residual degrees of freedom
- x_
-
the testing/full data/input matrix
- α
-
the significance levels to be used (defaults to
Fit.α_)
Attributes
- Inherited from:
- Fit
Convert QoF results into an array (of size 1) of Statistic for compatibility with the crossValidate method.
Convert QoF results into an array (of size 1) of Statistic for compatibility with the crossValidate method.
Value parameters
- qof
-
the Quality of Fit (QoF) results
Attributes
- Inherited from:
- Model
Return the coefficient of determination (R^2). Must call diagnose first.
Return the coefficient of determination (R^2). Must call diagnose first.
Attributes
- Inherited from:
- FitM
Return a basic report on a trained and tested multi-variate model.
Return a basic report on a trained and tested multi-variate model.
Value parameters
- ftMat
-
the matrix of qof values produced by the
Fittrait
Attributes
- Definition Classes
-
PredictorMV -> Model
- Inherited from:
- PredictorMV
Return a basic report on a trained and tested model.
Return a basic report on a trained and tested model.
Value parameters
- ftVec
-
the vector of qof values produced by the
Fittrait
Attributes
- Inherited from:
- Model
Reset the best-step to default
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.
Value parameters
- df_update
-
the updated degrees of freedom (regression/model, error)
Attributes
- Inherited from:
- Fit
Return the matrix of residuals/errors.
Screen the x-columns of matrix xy based on the two thresholds, returning the reduced matrix and the column indices/predictor variables selected.
Screen the x-columns of matrix xy based on the two thresholds, returning the reduced matrix and the column indices/predictor variables selected.
Value parameters
- dep
-
the variable/column dependency measure (defaults to correlation)
- thr1
-
the threshold used to compare the predictor x-columns to the y-column only want variables above some minimal dependency level
- thr2
-
the threshold used to compare the predictor x-columns with each other only want variables below some cut-off dependency/collinearity level
- xy
-
the [ x, y ] combined data-response matrix
Attributes
- Inherited from:
- Model
Perform feature selection to find the most predictive features/variables to have in the model, returning the features/variables added and the new Quality of Fit (QoF) measures/metrics for all steps.
Perform feature selection to find the most predictive features/variables to have in the model, returning the features/variables added and the new Quality of Fit (QoF) measures/metrics for all steps.
Value parameters
- cross
-
indicator to include the cross-validation/validation QoF measure (defaults to "many")
- first
-
first variable to consider for elimination (default (1) assume intercept x_0 will be in any model)
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
- swap
-
whether to allow a swap step (swap out a feature for a new feature in one step)
- tech
-
the feature selection technique to apply
Attributes
- See also
-
Fitfor index of QoF measures/metrics. - Inherited from:
- FeatureSelection
Show the QoF metrics/measures in vector qof.
Show the QoF metrics/measures in vector qof.
Value parameters
- qof
-
the QoF metrics (e.g., for point and interval predictions/forecasts)
Attributes
- Inherited from:
- Fit
Return the sum of the squares for error (sse). Must call diagnose first.
Return the sum of the squares for error (sse). Must call diagnose first.
Attributes
- Inherited from:
- FitM
Perform STEPWISE SELECTION to find a GOOD COMBINATION of predictive variables to have in the model, returning the variables selected and the new Quality of Fit (QoF) measures for all steps. At each step it calls 'forwardSel' and 'backwardElim' and takes the best of the two actions. Stops when neither action yields improvement.
Perform STEPWISE SELECTION to find a GOOD COMBINATION of predictive variables to have in the model, returning the variables selected and the new Quality of Fit (QoF) measures for all steps. At each step it calls 'forwardSel' and 'backwardElim' and takes the best of the two actions. Stops when neither action yields improvement.
Value parameters
- cross
-
indicator to include the cross-validation/validation QoF measure (defaults to "many")
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures. - Inherited from:
- PredictorMV
Produce a QoF SUMMARY for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). Note: `Fac_Cholesky is used to compute the inverse of xtx.
Produce a QoF SUMMARY for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). Note: `Fac_Cholesky is used to compute the inverse of xtx.
Value parameters
- b
-
the parameters/coefficients for the model
- fname
-
the array of feature/variable names
- vifs
-
the Variance Inflation Factors (VIFs)
- x_
-
the testing/full data/input matrix
Attributes
- Definition Classes
- Inherited from:
- Fit
Get the type of the task performed by model.
Test/evaluate the model's Quality of Fit (QoF) and return the predictions and QoF vectors. This may include the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classes should implement various diagnostics for the test and full (training + test) datasets.
Test/evaluate the model's Quality of Fit (QoF) and return the predictions and QoF vectors. This may include the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classes should implement various diagnostics for the test and full (training + test) datasets.
Value parameters
- x_
-
the testing/full data/input matrix (impl. classes may default to x)
- y_
-
the testing/full response/output vector (impl. classes may default to y)
Attributes
- Inherited from:
- PredictorMV
Return the indices for the test-set for (1) RANDONLY or (3) LAST
Return the indices for the test-set for (1) RANDONLY or (3) LAST
Value parameters
- n_test
-
the size of test-set
- n_total
-
the size of full dataset
- rando
-
whether to select indices randomly or in blocks
Attributes
- See also
-
scalation.mathstat.TnT_Split - Inherited from:
- PredictorMV
Return the indices for the test-set.
Return the indices for the test-set.
Value parameters
- n_test
-
the size of test-set
- rando
-
whether to select indices randomly or in blocks
Attributes
- See also
-
scalation.mathstat.TnT_Split - Inherited from:
- PredictorMV
Train the model 'y_ = f(x_) + e' on a given dataset, by optimizing the model parameters in order to minimize error '||e||' or maximize log-likelihood 'll'.
Train the model 'y_ = f(x_) + e' on a given dataset, by optimizing the model parameters in order to minimize error '||e||' or maximize log-likelihood 'll'.
Value parameters
- x_
-
the training/full data/input matrix (impl. classes may default to x)
- y_
-
the training/full response/output vector (impl. classes may default to y)
Attributes
- Inherited from:
- PredictorMV
Train and test the predictive model y_ = f(x_) + e and report its QoF and plot its predictions. FIX - currently must override if y is transformed, @see TranRegression
Train and test the predictive model y_ = f(x_) + e and report its QoF and plot its predictions. FIX - currently must override if y is transformed, @see TranRegression
Value parameters
- x_
-
the training/full data/input matrix (defaults to full x)
- xx
-
the testing/full data/input matrix (defaults to full x)
- y_
-
the training/full response/output matrix (defaults to full y)
- yy
-
the testing/full response/output matrix (defaults to full y)
Attributes
- Inherited from:
- PredictorMV
Train and test the predictive model y_ = f(x_) + e and report its QoF and plot its predictions. This version does auto-tuning. FIX - currently must override if y is transformed, @see TranRegression
Train and test the predictive model y_ = f(x_) + e and report its QoF and plot its predictions. This version does auto-tuning. FIX - currently must override if y is transformed, @see TranRegression
Value parameters
- x_
-
the training/full data/input matrix (defaults to full x)
- xx
-
the testing/full data/input matrix (defaults to full x)
- y_
-
the training/full response/output matrix (defaults to full y)
- yy
-
the testing/full response/output matrix (defaults to full y)
Attributes
- Inherited from:
- PredictorMV
Attributes
- Inherited from:
- PredictorMV
Compute the Variance Inflation Factor (VIF) for each variable to test for multi-collinearity by regressing x_j against the rest of the variables. A VIF over 50 indicates that over 98% of the variance of x_j can be predicted from the other variables, so x_j may be a candidate for removal from the model. Note: override this method to use a superior regression technique.
Compute the Variance Inflation Factor (VIF) for each variable to test for multi-collinearity by regressing x_j against the rest of the variables. A VIF over 50 indicates that over 98% of the variance of x_j can be predicted from the other variables, so x_j may be a candidate for removal from the model. Note: override this method to use a superior regression technique.
Value parameters
- skip
-
the number of columns of x at the beginning to skip in computing VIF
Attributes
- Inherited from:
- PredictorMV
Inherited fields
The optional reference to an ontological concept