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Predict responses using regression tree model



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 arguments in the previous syntaxes.


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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

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

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 PredictorNamesname-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.

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

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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 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

Version History

Introduced in R2011a