RegressionChainEnsemble
Description
RegressionChainEnsemble
is a trained multiresponse regression
model that uses regression chains. Use the predict
and
loss
object
functions to predict on new data and compute the regression loss, respectively.
For more information, see Regression Chains.
Creation
Create a RegressionChainEnsemble
object by using the fitrchains
function.
Properties
Chain Ensemble Properties
ChainOrders
— Order of response variables in regression chains
positive integer matrix
This property is read-only.
Order of the response variables in the regression chains, specified as a positive integer matrix. Row i indicates the order of the response variables in regression chain i.
Data Types: double
Learners
— Compact regression models trained as part of regression chains
cell array of regression model objects
This property is read-only.
Compact regression models trained as part of the regression chains, specified as a cell array
of regression model objects. Each row of Learners
corresponds to
one regression chain.
This table lists the possible compact regression models.
Regression Model Type | Model Object |
---|---|
Bagged or boosted ensemble of trees | CompactRegressionEnsemble |
General additive model (GAM) | CompactRegressionGAM |
Gaussian process regression (GPR) | CompactRegressionGP |
Kernel model | RegressionKernel |
Linear model | RegressionLinear |
Support vector machine (SVM) | CompactRegressionSVM |
Decision tree | CompactRegressionTree |
Data Types: cell
NumChains
— Number of regression chains
positive integer scalar
This property is read-only.
Number of regression chains in the chain ensemble, specified as a positive integer scalar.
NumChains
indicates the number of rows in
ChainOrders
and Learners
.
Data Types: double
Data Properties
CategoricalPredictors
— Categorical predictor indices
positive integer vector | []
This property is read-only.
Categorical predictor indices, specified as a positive integer vector. Each index value in CategoricalPredictors
indicates that the corresponding predictor listed in PredictorNames
is categorical. If none of the predictors are categorical, then this property is empty ([]
).
Data Types: double
NumObservations
— Number of observations
positive integer scalar
This property is read-only.
Number of observations in the data stored in X
and
Y
, specified as a positive integer scalar.
Data Types: double
NumPredictors
— Number of predictor variables
positive integer scalar
This property is read-only.
Number of predictor variables, specified as a positive integer scalar.
NumPredictors
does not include response variables that are used
as predictors by some models in Learners
.
To see all the predictors used by a specific compact regression model in
Learners
, use the properties of the compact regression model.
For an example, see Specify Multiresponse Regression Model Properties.
Data Types: double
NumResponses
— Number of response variables
positive integer scalar
This property is read-only.
Number of response variables, specified as a positive integer scalar.
Data Types: double
PredictorNames
— Predictor variable names
cell array of character vectors
This property is read-only.
Predictor variable names, specified as a cell array of character vectors. The order of the
elements in PredictorNames
corresponds to the order of the
predictors in the data used to train the model.
Data Types: cell
ResponseName
— Response variable names
string array
This property is read-only.
Response variable names, specified as a string array. The order of the elements in
ResponseName
corresponds to the order of the response variables
in the data used to train the model.
Data Types: string
X
— Predictor data
numeric matrix | table
This property is read-only.
Predictor data used to train the model, specified as a numeric matrix or a table.
Each row of X
corresponds to an observation, and each column
corresponds to a predictor variable (PredictorNames
).
Data Types: single
| double
| table
Y
— Response data
numeric matrix | numeric table
This property is read-only.
Response data used to train the model, specified as a numeric matrix or table.
Each row of Y
corresponds to an observation, and each column
corresponds to a response variable (ResponseName
).
Data Types: single
| double
| table
W
— Observation weights
numeric vector
This property is read-only.
Observation weights used to train the model, specified as a numeric vector. Each
row of W
corresponds to an observation.
Data Types: single
| double
Object Functions
Examples
Specify Multiresponse Regression Model Properties
Train a multiresponse regression model using regression chains. Specify the type of regression models to use in the regression chains, and train the models with predicted values for response variables used as predictors.
Load the carbig
data set, which contains measurements of cars made in the 1970s and early 1980s. Create a table containing the predictor variables Displacement
, Horsepower
, and so on, as well as the response variables Acceleration
and MPG
. Display the first eight rows of the table.
load carbig cars = table(Displacement,Horsepower,Model_Year, ... Origin,Weight,Acceleration,MPG); head(cars)
Displacement Horsepower Model_Year Origin Weight Acceleration MPG ____________ __________ __________ _______ ______ ____________ ___ 307 130 70 USA 3504 12 18 350 165 70 USA 3693 11.5 15 318 150 70 USA 3436 11 18 304 150 70 USA 3433 12 16 302 140 70 USA 3449 10.5 17 429 198 70 USA 4341 10 15 454 220 70 USA 4354 9 14 440 215 70 USA 4312 8.5 14
Categorize the cars based on whether they were made in the USA.
cars.Origin = categorical(cellstr(cars.Origin)); cars.Origin = mergecats(cars.Origin,["France","Japan",... "Germany","Sweden","Italy","England"],"NotUSA");
Remove observations with missing values.
cars = rmmissing(cars);
Train a multiresponse regression model by passing the cars
data to the fitrchains
function. Use regression chains composed of regression support vector machine (SVM) models with standardized numeric predictors. When training the SVM models, use the predicted values for the response variables that are treated as predictors.
Mdl = fitrchains(cars,["Acceleration","MPG"], ... Learner=templateSVM(Standardize=true), ... ChainPredictedResponse=true);
Mdl
is a trained RegressionChainEnsemble
model object. You can use dot notation to access the properties of Mdl
.
Display the order of the response variables in the regression chains in Mdl
, and display the trained regression SVM models in the regression chains.
Mdl.ChainOrders
ans = 2×2
1 2
2 1
Mdl.Learners
ans=2×2 cell array
{1x1 classreg.learning.regr.CompactRegressionSVM} {1x1 classreg.learning.regr.CompactRegressionSVM}
{1x1 classreg.learning.regr.CompactRegressionSVM} {1x1 classreg.learning.regr.CompactRegressionSVM}
In the first regression chain, the first SVM model uses Acceleration
as the response variable. The second SVM model uses MPG
as the response variable and the predicted values for Acceleration
as a predictor variable. The first SVM model provides the predicted Acceleration
values used by the second SVM model.
Recall that the SVM models use standardized numeric predictors. Find the means (Mu
) and standard deviations (Sigma
) used by the second model in the first regression chain.
Chain1Model2 = Mdl.Learners{1,2}; Mdl.PredictorNames
ans = 1x5 cell
{'Displacement'} {'Horsepower'} {'Model_Year'} {'Origin'} {'Weight'}
Chain1Model2.ExpandedPredictorNames
ans = 1x7 cell
{'x1'} {'x2'} {'x3'} {'x4 == 1'} {'x4 == 2'} {'x5'} {'x6'}
Chain1Model2.Mu
ans = 1×7
103 ×
0.1944 0.1045 0.0760 0 0 2.9776 0.0153
Chain1Model2.Sigma
ans = 1×7
104.6440 38.4912 3.6837 1.0000 1.0000 849.4026 2.2190
The SVM model uses five numeric predictors: Displacement
(x1
), Horsepower
(x2
), Model_Year
(x3
), Weight
(x5
), and the predicted values for Acceleration
(x6
). The software uses the corresponding Mu
and Sigma
values to standardize the predictor data before predicting with the predict
object function.
The categorical predictor Origin
is split into two variables (x4 == 1
and x4 == 2
) after categorical expansion. The corresponding Mu
and Sigma
values indicate that the two variables are unchanged after standardization.
Version History
Introduced in R2024b
See Also
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