The Predictor trait provides a framwork for multiple predictive analytics techniques, e.g., Regression. x is multi-dimensional [1, x_1, ... x_k]. Fit the parameter vector b in for example the regression equation y = b dot x + e = b_0 + b_1 * x_1 + ... b_k * x_k + e
Value parameters
- fname
-
the feature/variable names (if null, use x_j's)
- hparam
-
the hyper-parameters for the model
- x
-
the input/data m-by-n matrix (augment with a first column of ones to include intercept in model)
- y
-
the response/output m-vector
Attributes
- Companion
- object
- Graph
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- Supertypes
- Known subtypes
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class ClusteringPredictorclass ELM_3L1class ExpRegressionclass KNN_Regressionclass LassoRegressionclass NonlinearRegressionclass NullModelclass Perceptronclass PoissonRegressionclass Regressionclass ARXclass ARX_Quadclass PolyORegressionclass PolyRegressionclass RegressionCatclass RegressionWLSclass RoundRegressionclass TranRegressionclass TrigRegressionclass RegressionTreeclass RegressionTreeGBclass RegressionTreeMTclass RegressionTreeRFclass RidgeRegressionclass SimpleExpRegressionclass SimpleRegressionclass SimplerRegressionShow all
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Type members
Classlikes
The BestStep is used to record the best improvement step found so far.
The BestStep is used to record the best improvement step found so far.
Value parameters
- col
-
the column/variable to ADD/REMOVE for this step
- mod
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the model including selected features/variables for this step
- qof
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the Quality of Fit (QoF) for this step
Attributes
- Supertypes
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trait Serializabletrait Producttrait Equalsclass Objecttrait Matchableclass AnyShow all
Value members
Abstract methods
Build a sub-model that is restricted to the given columns of the data matrix. Must be implemented for models that support feature selection. Otherwise, use @see `NoBuildModel
Build a sub-model that is restricted to the given columns of the data matrix. Must be implemented for models that support feature selection. Otherwise, use @see `NoBuildModel
Value parameters
- x_cols
-
the columns that the new model is restricted to
Attributes
Test the predictive model y_ = f(x_) + e and return its predictions and QoF vector. Testing may be in-sample (on the full dataset) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train before test.
Test the predictive model y_ = f(x_) + e and return its predictions and QoF vector. Testing may be in-sample (on the full dataset) 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 vector (defaults to full y)
Attributes
Train a predictive model y_ = f(x_) + e where x_ is the data/input matrix and y_ is the response/output vector. These arguments default to the full dataset x and y, but may be restricted to a training dataset. Training involves estimating the model parameters b.
Train a predictive model y_ = f(x_) + e where x_ is the data/input matrix and y_ is the response/output vector. These arguments default to the full dataset x and y, but may be restricted to a training dataset. Training involves estimating the model parameters b.
Value parameters
- x_
-
the training/full data/input matrix (defaults to full x)
- y_
-
the training/full response/output vector (defaults to full y)
Attributes
Concrete methods
Perform backward elimination to find the least predictive variable to remove from the existing model, returning the variable to eliminate, the new parameter vector 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 vector 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)
- idx_q
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
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Fitfor index of QoF measures.
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
-
whether to include the cross-validation QoF measure
- first
-
first variable to consider for elimination
- idx_q
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures.
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
- idx_q
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures.
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
-
whether to include the cross-validation QoF measure
- idx_q
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures.
Return the best model found from feature selection.
Return the best model found from feature selection.
Attributes
Return the feature/variable names.
Return the feature/variable names.
Attributes
Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_, e.g., SymbolicRegression.quadratic adds squared columns.
Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_, e.g., SymbolicRegression.quadratic adds squared columns.
Attributes
Return the used response vector y. Mainly for derived classes where y is transformed, e.g., TranRegression, ARX.
Return the used response vector y. Mainly for derived classes where y is transformed, e.g., TranRegression, ARX.
Attributes
Return the hyper-parameters.
Return the hyper-parameters.
Attributes
Return the relative importance of selected variables, ordered highest to lowest, rescaled so the highest is one.
Return the relative importance of selected variables, ordered highest to lowest, rescaled so the highest is one.
Value parameters
- cols
-
the selected columns/features/variables
- rSq
-
the matrix R^2 values (stand in for sse)
Attributes
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
Return the vector of parameter/coefficient values.
Return the vector of parameter/coefficient values.
Attributes
Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2). Must override when using transformations, e.g., ExpRegression.
Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2). Must override when using transformations, e.g., ExpRegression.
Value parameters
- z
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the new vector to predict
Attributes
Predict the value of vector y = f(x_, b), e.g., x_ * b for Regression. May override for efficiency.
Predict the value of vector y = f(x_, b), e.g., x_ * b for Regression. May override for efficiency.
Value parameters
- x_
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the matrix to use for making predictions, one for each row
Attributes
Reset the best-step to default
Reset the best-step to default
Attributes
Return the vector of residuals/errors.
Return the vector of residuals/errors.
Attributes
Perform feature 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 feature 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
-
whether to include the cross-validation QoF measure
- idx_q
-
index of Quality of Fit (QoF) to use for comparing quality
- tech
-
the feature selection technique to apply
Attributes
- See also
-
Fitfor index of QoF measures.
Perform stepwise regression to find the most predictive variables to have in the model, returning the variables left 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 regression to find the most predictive variables to have in the model, returning the variables left 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
-
whether to include the cross-validation QoF measure
- idx_q
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures.
Return the indices for the test-set.
Return the indices for the test-set.
Value parameters
- n_test
-
the size of test-set
- rando
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whether to select indices randomly or in blocks
Attributes
- See also
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scalation.mathstat.TnT_Split
The train2 method should work like the train method, but should also optimize hyper-parameters (e.g., shrinkage or learning rate). Only implementing classes needing this capability should override this method.
The train2 method should work like the train method, but should also optimize hyper-parameters (e.g., shrinkage or learning rate). Only implementing classes needing this capability should override this method.
Value parameters
- x_
-
the training/full data/input matrix (defaults to full x)
- y_
-
the training/full response/output vector (defaults to full y)
Attributes
Train and test the predictive model y_ = f(x_) + e and report its QoF and plot its predictions. Return the predictions and QoF. 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. Return the predictions and QoF. 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 vector (defaults to full y)
- yy
-
the testing/full response/output vector (defaults to full y)
Attributes
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 methods
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
- Inherited from:
- Model
Inherited fields
The optional reference to an ontological concept