Predict responses using regression tree
Yfit = predict(Mdl,X,Name,Value)Name,Value pair
arguments. For example, you can specify to prune Mdl to
a particular level before predicting responses.
Mdl — Trained regression treeRegressionTree model object | CompactRegressionTree model objectTrained classification tree, specified as a RegressionTree or CompactRegressionTree model
object. That is, Mdl is a trained classification
model returned by fitrtree or compact.
X — Predictor data to be classifiedPredictor data to be classified, specified as a numeric matrix or table.
Each row of X corresponds to one observation,
and each column corresponds to one variable.
For a numeric matrix:
The variables making up the columns of X must
have the same order as the predictor variables that trained Mdl.
If you trained Mdl using a table (for example, Tbl),
then X can be a numeric matrix if
Tbl contains all numeric
predictor variables. To treat numeric predictors in
Tbl as categorical during
training, identify categorical predictors using the
CategoricalPredictors name-value pair
argument of fitrtree.
If Tbl contains heterogeneous
predictor variables (for example, numeric and
categorical data types) and X is a
numeric matrix, then predict throws
an error.
For a table:
predict does not support multi-column
variables and cell arrays other than cell arrays of character vectors.
If you trained Mdl using a table
(for example, Tbl), then all predictor variables
in X must have the same variable names and data
types as those that trained Mdl (stored in Mdl.PredictorNames).
However, the column order of X does not need to
correspond to the column order of Tbl. Tbl and X can
contain additional variables (response variables, observation weights,
etc.), but predict ignores them.
If you trained Mdl using a numeric matrix, then the predictor names in
Mdl.PredictorNames and
corresponding predictor variable names in
X must be the same. To specify
predictor names during training, see the PredictorNames name-value pair argument
of fitrtree. All predictor
variables in X must be numeric
vectors. X can contain additional
variables (response variables, observation weights,
etc.), but predict ignores
them.
Data Types: table | double | single
Specify optional
comma-separated pairs of Name,Value arguments. Name is
the argument name and Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN.
'Subtrees' — Pruning level'all'Pruning level, specified as the comma-separated pair consisting
of 'Subtrees' and a vector of nonnegative integers
in ascending order or 'all'.
If you specify a vector, then all elements must be at least 0 and
at most max(Mdl.PruneList). 0 indicates
the full, unpruned tree and max(Mdl.PruneList) indicates
the completely pruned tree (i.e., just the root node).
If you specify 'all', then predict operates
on all subtrees (i.e., the entire pruning sequence). This specification
is equivalent to using 0:max(Mdl.PruneList).
predict prunes Mdl to
each level indicated in Subtrees, and then estimates
the corresponding output arguments. The size of Subtrees determines
the size of some output arguments.
To invoke Subtrees, the properties PruneList and PruneAlpha of Mdl must
be nonempty. In other words, grow Mdl by setting 'Prune','on',
or by pruning Mdl using prune.
Example: 'Subtrees','all'
Data Types: single | double | char | string
Load the carsmall data set. Consider Displacement, Horsepower, and Weight as predictors of the response MPG.
load carsmall
X = [Displacement Horsepower Weight];Grow a regression tree using the entire data set.
Mdl = fitrtree(X,MPG);
Predict the MPG for a car with 200 cubic inch engine displacement, 150 horsepower, and that weighs 3000 lbs.
X0 = [200 150 3000]; MPG0 = predict(Mdl,X0)
MPG0 = 21.9375
The regression tree predicts the car's efficiency to be 21.94 mpg.
This function fully supports tall arrays. You can use models trained on either in-memory or tall data with this function.
For more information, see Tall Arrays.
Usage notes and limitations:
You can generate C/C++ code for both predict and
update by using a coder configurer. Or, generate code only for
predict by using saveLearnerForCoder,
loadLearnerForCoder, and codegen.
Code generation for predict and update — Create a coder configurer by using learnerCoderConfigurer and then generate code by using generateCode. Then you can update model parameters in the
generated code without having to regenerate the code.
Code generation for predict — Save a trained model by
using saveLearnerForCoder. Define an
entry-point function that loads the saved model by using loadLearnerForCoder and calls the
predict function. Then use codegen (MATLAB Coder) to generate code for the
entry-point function.
You can also generate single-precision C/C++ code for
predict. For single-precision code generation, specify the
name-value pair argument 'DataType','single' as an additional input to the
loadLearnerForCoder function.
You can also generate fixed-point C/C++ code for
predict. Fixed-point code generation requires an additional step that
defines the fixed-point data types of the variables required for prediction. Create a
fixed-point data type structure by using the data type function
generated by generateLearnerDataTypeFcn, and use the structure as an input argument of
loadLearnerForCoder in an entry-point function. Generating fixed-point
C/C++ code requires MATLAB®
Coder™ and Fixed-Point Designer™.
This table contains
notes about the arguments of predict. Arguments not included in this
table are fully supported.
| Argument | Notes and Limitations |
|---|---|
Mdl | For the usage notes and limitations of the model object,
see
Code Generation of the
|
X |
|
Subtrees |
|
For more information, see Introduction to Code Generation.
compact | CompactRegressionTree | fitrtree | loss | RegressionTree
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