The RegressionTreeRF4TS class supports Random Forest for Time Series data. Multi-horizon forecasting supported via the Recursive method. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.
y_t = f(x)
where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}].
Value parameters
- fname
-
the feature/variable names
- hparam
-
the hyper-parameters (use RegressionTree.hp for default)
- lags
-
the maximum lag included (inclusive)
- use_fb
-
whether to use feature bagging (select subsets of the features)
- x
-
the input/predictor matrix built out of lags of y (and optionally from exogenous variables ex)
- yy
-
the output/response vector trimmed to match x.dim
Attributes
- Companion
- object
- Graph
-
- Supertypes
-
trait ForecasterXclass RegressionTreeRFtrait Fittrait FitMtrait Predictortrait FeatureSelectiontrait Modelclass Objecttrait Matchableclass AnyShow all
Members list
Value members
Concrete methods
Produce a vector of size h, of 1 through h-steps ahead forecasts for the model. forecast the following time points: t+1, ..., t-1+h. Note, must create the yf matrix before calling the forecast method. Intended to work with rolling validation (analog of predict method) Must call forecastAll first.
Produce a vector of size h, of 1 through h-steps ahead forecasts for the model. forecast the following time points: t+1, ..., t-1+h. Note, must create the yf matrix before calling the forecast method. Intended to work with rolling validation (analog of predict method) Must call forecastAll first.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- t
-
the time point from which to make forecasts
- yf
-
the forecast matrix (time x horizons)
Attributes
Forecast values for all y_.dim time points at horizon h (h-steps ahead). Assign to FORECAST MATRIX and return h-step ahead forecast. Note, predictAll provides predictions for h = 1.
Forecast values for all y_.dim time points at horizon h (h-steps ahead). Assign to FORECAST MATRIX and return h-step ahead forecast. Note, predictAll provides predictions for h = 1.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- yf
-
the forecast matrix for the endogenous variable y (time x horizons)
- yx
-
the matrix of endogenous y and exogenous x values
Attributes
- See also
-
forecastAllmethod inForecastertrait.
Get the internally row trimed and column expanded input matrix and response vector.
Get the internally row trimed and column expanded input matrix and response vector.
Attributes
Predict a value for y_t+1 using the 1-step ahead forecast. y_t+1 = f (y_t, ...) + e_t+1
Predict a value for y_t+1 using the 1-step ahead forecast. y_t+1 = f (y_t, ...) + e_t+1
Value parameters
- t
-
the time point from which to make prediction
- yx
-
the matrix of endogenous y and exogenous x values
Attributes
Test FORECASTS of a RegressionTreeRF4TS forecasting model y_ = f(x) + e and return its forecasts and QoF vector. Testing may 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 and forecastAll before testF.
Test FORECASTS of a RegressionTreeRF4TS forecasting model y_ = f(x) + e and return its forecasts and QoF vector. Testing may 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 and forecastAll before testF.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- y_
-
the testing/full response/output vector
- yx
-
the matrix of endogenous y and exogenous x values
Attributes
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 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)
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- See also
-
Fitfor index of QoF measures. - Inherited from:
- Predictor
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:
- Predictor
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 features (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 features (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:
- Predictor
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
- Inherited from:
- RegressionTreeRF
Attributes
- Inherited from:
- Predictor
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
Forecast values for all y_.dim time points and all horizons (1 through h-steps ahead). Record these in the FORECAST MATRIX yf, where yf(t, k) = k-steps ahead forecast for y_t Note, column 0, yf(?, 0), is set to y (the actual time-series values). last column, yf(?, h+1), is set to t (the time values, for reference). Forecast recursively down diagonals in the yf forecast matrix. The top right and bottom left triangles in yf matrix are not forecastable.
Forecast values for all y_.dim time points and all horizons (1 through h-steps ahead). Record these in the FORECAST MATRIX yf, where yf(t, k) = k-steps ahead forecast for y_t Note, column 0, yf(?, 0), is set to y (the actual time-series values). last column, yf(?, h+1), is set to t (the time values, for reference). Forecast recursively down diagonals in the yf forecast matrix. The top right and bottom left triangles in yf matrix are not forecastable.
Value parameters
- h
-
the maximum forecasting horizon, number of steps ahead to produce forecasts
- y_
-
the actual values to use in making forecasts
- yx
-
the matrix of endogenous y and exogenous x values
Attributes
- Inherited from:
- ForecasterX
Forecast intervals for all y_.dim time points at horizon h (h-steps ahead). Create prediction intervals (two vectors) for the given time points at level p.
Forecast intervals for all y_.dim time points at horizon h (h-steps ahead). Create prediction intervals (two vectors) for the given time points at level p.
Value parameters
- h
-
the forecasting horizon, number of steps ahead to produce forecasts
- p
-
the level (1 - alpha) for the prediction interval
- y_
-
the aligned actual values to use in making forecasts
- yfh
-
the forecast vector at horizon h
Attributes
- Inherited from:
- ForecasterX
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:
- Predictor
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:
- Predictor
Run the full model before variable elimination as a starting point for backward elimination.
Run the full model before variable elimination as a starting point for backward elimination.
Value parameters
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- Inherited from:
- Predictor
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_, 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
- Inherited from:
- Predictor
Return the data matrix x concatenated with response vector y.
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
- Inherited from:
- Predictor
Return the y-transformation.
Return the used response matrix y, if needed.
Return the used response matrix y, if needed.
Attributes
- See also
-
neuralnet.PredictorMV - Inherited from:
- Model
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.
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
- Inherited from:
- Predictor
Perform In-Sample Testing, i.e., train and test on the same FULL data set. Return the prediction and the Quality of Fit.
Perform In-Sample Testing, i.e., train and test on the same 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:
- Predictor
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:
- Predictor
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:
- Predictor
Return the vector of parameter/coefficient values.
Predict the response value given vector z by averaging the predictions over all the randomized trees.
Predict the response value given vector z by averaging the predictions over all the randomized trees.
Value parameters
- z
-
the vector to be predicted
Attributes
- Definition Classes
- Inherited from:
- RegressionTreeRF
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_
-
the matrix to use for making predictions, one for each row
Attributes
- Inherited from:
- Predictor
Produce a conformal PREDICTION INTERVAL half width for each prediction yp (y-hat). Implements Algorithm 2 Split Conformal Prediction from
Produce a conformal PREDICTION INTERVAL half width for each prediction yp (y-hat). Implements Algorithm 2 Split Conformal Prediction from
Value parameters
- x_
-
the testing/full data/input matrix
- y_
-
the testing/full response/output vector
- α
-
the significance level (1 - p_)
Attributes
- See also
- Inherited from:
- Predictor
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
- Inherited from:
- Model
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
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
Evalaute the model with only one column, e.g., intercept only model.
Evalaute the model with only one column, e.g., intercept only model.
Value parameters
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- Inherited from:
- Predictor
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
- swap
-
whether to allow a swap step (swap out a feature for a new feature in one step)
Attributes
- See also
-
Fitfor index of QoF measures.modeling.FeatureSelectionfor GIVENS for qk and slack_base - Inherited from:
- Predictor
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
Swap out variable with in variable.
Swap out variable with in variable.
Value parameters
- cols
-
the columns of matrix x currently included in the existing model
- in
-
the variable to swap in
- out
-
the variable to swap out
- qk
-
index of Quality of Fit (QoF) to use for comparing quality
Attributes
- Inherited from:
- Predictor
Get the type of the task performed by model.
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 vector (defaults to full y)
Attributes
- Inherited from:
- RegressionTreeRF
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:
- Predictor
Return the indices for the test-set for (1) RANDONLY or (2) FIRST.
Return the indices for the test-set for (1) RANDONLY or (2) FIRST.
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:
- Predictor
Train the regression tree RF by selecting thresholds for the features/variables in matrix x_. Build the trees of the forest by selecting a subSample for each tree.
Train the regression tree RF by selecting thresholds for the features/variables in matrix x_. Build the trees of the forest by selecting a subSample for each tree.
Value parameters
- x_
-
the training/full data/input matrix
- y_
-
the training/full response/output vector
Attributes
- Inherited from:
- RegressionTreeRF
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
- Inherited from:
- Predictor
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
- Inherited from:
- Predictor
Attributes
- Inherited from:
- Predictor
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:
- Predictor
Inherited fields
The optional reference to an ontological concept