predict

Predict responses using regression tree model

Syntax

Yfit = predict(tree,X)

Yfit = predict(tree,X,Subtrees=subtrees)

[Yfit,node] = predict(___)

Description

Yfit = predict(tree,X) returns a vector of predicted responses for the predictor data in the table or matrix X, based on the trained regression tree tree.

Yfit = predict(tree,X,Subtrees=subtrees) also prunes tree to the level specified by subtrees, before predicting responses.

[Yfit,node] = predict(___) also returns a vector of predicted node numbers for the responses, using any of the input argument combinations in the previous syntaxes.

Examples

Predict a Response Using a Regression Tree

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.

Input Arguments

tree — Trained regression tree

RegressionTree model object | CompactRegressionTree model object

Trained regression tree, specified as a RegressionTree model object trained with fitrtree, or a CompactRegressionTree model object created with compact.

X — Predictor data

numeric matrix | table

Predictor data used to predict responses, specified as a numeric matrix or a table.

Each row of X corresponds to one observation, and each column corresponds to one variable.

For a numeric matrix:

• The variables that make up the columns of X must have the same order as the predictor variables used to train tree.

• If you train tree using a table (for example, Tbl), X can be a numeric matrix if Tbl contains only numeric predictor variables. To treat numeric predictors in Tbl as categorical during training, specify categorical predictors using the CategoricalPredictors name-value argument of fitrtree. If Tbl contains heterogeneous predictor variables (for example, numeric and categorical data types) and X is a numeric matrix, predict issues an error.

For a table:

• predict does not support multicolumn variables or cell arrays other than cell arrays of character vectors.

• If you train tree using a table (for example, Tbl), all predictor variables in X must have the same variable names and data types as those used to train tree (stored in tree.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, such as response variables and observation weights, but predict ignores them.

• If you train tree using a numeric matrix, the predictor names in tree.PredictorNames must be the same as the corresponding predictor variable names in X. To specify predictor names during training, use the PredictorNames name-value argument of fitrtree. All predictor variables in X must be numeric vectors. X can contain additional variables, such as response variables and observation weights, but predict ignores them.

Data Types: single | double | table

subtrees — Pruning level

0 (default) | vector of nonnegative integers | "all"

Pruning level, specified as 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(tree.PruneList). 0 indicates the full, unpruned tree, and max(tree.PruneList) indicates the completely pruned tree (that is, just the root node).

If you specify "all", then predict operates on all subtrees (that is, the entire pruning sequence). This specification is equivalent to using 0:max(tree.PruneList).

predict prunes tree to each level specified by subtrees, and then estimates the corresponding output arguments. The size of subtrees determines the size of some output arguments.

For the function to invoke subtrees, the properties PruneList and PruneAlpha of tree must be nonempty. In other words, grow tree by setting Prune="on" when you use fitrtree, or by pruning tree using prune.

Data Types: single | double | char | string

Output Arguments

Yfit — Predicted response values

numeric column vector

Predicted response values, returned as a numeric column vector with the same number of rows as X. Each row of Yfit gives the predicted response to the corresponding row of X, based on the regression model tree.

node — Node numbers

numeric vector

Node numbers for the predictions, returned as a numeric vector. Each entry corresponds to the predicted leaf node in tree for the corresponding row of X.

Alternative Functionality

Simulink Block

To integrate the prediction of a regression tree model into Simulink^®, you can use the RegressionTree Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB^® Function block with the predict function. For examples, see Predict Responses Using RegressionTree Predict Block and Predict Class Labels Using MATLAB Function Block.

When deciding which approach to use, consider the following:

• If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.

• Support for variable-size arrays must be enabled for a MATLAB Function block with the predict function.

• If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

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.

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

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.

• To generate single-precision C/C++ code for predict, specify the name-value argument "DataType","single" when you call 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 then 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
  tree	For the usage notes and limitations of the model object, see Code Generation of the CompactRegressionTree object.
  X	For general code generation, X must be a single-precision or double-precision matrix or a table containing numeric variables, categorical variables, or both. In the coder configurer workflow, X must be a single-precision or double-precision matrix. For fixed-point code generation, X must be a fixed-point matrix. The number of rows, or observations, in X can be a variable size, but the number of columns in X must be fixed. If you want to specify X as a table, then your model must be trained using a table, and your entry-point function for prediction must do the following: Accept data as arrays. Create a table from the data input arguments and specify the variable names in the table. Pass the table to predict. For an example of this table workflow, see Generate Code to Classify Data in Table. For more information on using tables in code generation, see Code Generation for Tables (MATLAB Coder) and Table Limitations for Code Generation (MATLAB Coder).
  Subtrees	Names in name-value arguments must be compile-time constants. For example, to allow user-defined pruning levels in the generated code, include {coder.Constant("Subtrees"),coder.typeof(0,[1,n],[0,1])} in the -args value of codegen (MATLAB Coder), where n is max(tree.PruneList). The Subtrees name-value argument is not supported in the coder configurer workflow. For fixed-point code generation, the Subtrees value must be coder.Constant("all") or have an integer data type.

For more information, see Introduction to Code Generation.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• The predict function does not support decision tree models trained with surrogate splits.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2011a