predict

Predict labels using classification tree model

Syntax

label = predict(tree,X)

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

[label,score,node,cnum] = predict(___)

Description

label = predict(tree,X) returns a vector of predicted class labels for the predictor data in the table or matrix X, based on the trained classification tree tree.

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

[label,score,node,cnum] = predict(___) also returns the following, using any of the input argument combinations in the previous syntaxes:

• A matrix of classification scores (score) indicating the likelihood that a label comes from a particular class. For classification trees, scores are posterior probabilities. For each observation in X, the predicted class label corresponds to the minimum expected misclassification cost among all classes.

• A vector of predicted node numbers for the classification (node).

• A vector of predicted class numbers for the classification (cnum).

Examples

Predict Labels Using Classification Tree

Examine predictions for a few rows in a data set left out of training.

Load Fisher's iris data set.

load fisheriris

Partition the data into training (50%) and validation (50%) sets.

n = size(meas,1);
rng(1) % For reproducibility
idxTrn = false(n,1);
idxTrn(randsample(n,round(0.5*n))) = true;
idxVal = idxTrn == false;                 

Grow a classification tree using the training set.

Mdl = fitctree(meas(idxTrn,:),species(idxTrn));

Predict labels for the validation data, and display several predicted labels. Count the number of misclassified observations.

label = predict(Mdl,meas(idxVal,:));
label(randsample(numel(label),5))

ans = 5x1 cell
    {'setosa'    }
    {'setosa'    }
    {'setosa'    }
    {'virginica' }
    {'versicolor'}

numMisclass = sum(~strcmp(label,species(idxVal)))

numMisclass = 
3

The software misclassifies three out-of-sample observations.

Estimate Class Posterior Probabilities Using Classification Tree

Load Fisher's iris data set.

load fisheriris

Partition the data into training (50%) and validation (50%) sets.

n = size(meas,1);
rng(1) % For reproducibility
idxTrn = false(n,1);
idxTrn(randsample(n,round(0.5*n))) = true;
idxVal = idxTrn == false;

Grow a classification tree using the training set, and then view it.

Mdl = fitctree(meas(idxTrn,:),species(idxTrn));
view(Mdl,"Mode","graph")

The resulting tree has four levels.

Estimate posterior probabilities for the test set using subtrees pruned to levels 1 and 3. Display several posterior probabilities.

[~,Posterior] = predict(Mdl,meas(idxVal,:), ...
    Subtrees=[1 3]);
Mdl.ClassNames

ans = 3x1 cell
    {'setosa'    }
    {'versicolor'}
    {'virginica' }

Posterior(randsample(size(Posterior,1),5),:,:)

ans = 
ans(:,:,1) =

    1.0000         0         0
    1.0000         0         0
    1.0000         0         0
         0         0    1.0000
         0    0.8571    0.1429


ans(:,:,2) =

    0.3733    0.3200    0.3067
    0.3733    0.3200    0.3067
    0.3733    0.3200    0.3067
    0.3733    0.3200    0.3067
    0.3733    0.3200    0.3067

The elements of Posterior are class posterior probabilities:

• Rows correspond to observations in the validation set.

• Columns correspond to the classes as listed in Mdl.ClassNames.

• Pages correspond to the subtrees.

The subtree pruned to level 1 is more sure of its predictions than the subtree pruned to level 3 (that is, the root node).

Input Arguments

tree — Trained classification tree

ClassificationTree model object | CompactClassificationTree model object

Trained classification tree, specified as a ClassificationTree model object trained with fitctree, or a CompactClassificationTree model object created with compact.

X — Predictor data

numeric matrix | table

Predictor data to be classified, 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), 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 argument of fitctree. If Tbl contains heterogeneous predictor variables (for example, numeric and categorical data types) and X is a numeric matrix, then 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), then 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 (response variables, observation weights, and so on), but predict ignores them.

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

Data Types: table | double | single

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 fitctree, or by pruning tree using prune.

Example: subtrees="all"

Data Types: single | double | char | string

Output Arguments

label — Predicted class labels

categorical array | character array | logical vector | vector of numeric values | cell array of character vectors

Predicted class labels, returned as a categorical or character array, logical or numeric vector, or cell array of character vectors. Each entry of label corresponds to the class with the minimal expected cost for the corresponding row of X.

Suppose subtrees is a numeric vector containing T elements, and X has N rows.

• If the response data type is char and T = 1, then label is a character matrix containing N rows. Each row contains the predicted label produced by subtrees.

• If the response data type is char and T > 1, then label is an N-by-T cell array. Column j of label contains the vector of predicted labels produced by subtree subtrees(j).

• Otherwise, label is an N-by-T array that has the same data type as the response. Column j of label contains the vector of predicted labels produced by subtree subtrees(j). (The software treats string arrays as cell arrays of character vectors.)

score — Posterior probabilities

numeric matrix

Posterior probabilities, returned as a numeric matrix of size N-by-K, where N is the number of observations (rows) in X, and K is the number of classes (in tree.ClassNames). score(i,j) is the posterior probability that row i in X is of class j in tree.ClassNames.

If subtrees has T elements, and X has N rows, then score is an N-by-K-by-T array, and node and cnum are N-by-T matrices.

node — Node numbers

numeric vector

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

cnum — Class numbers

numeric vector

Class numbers corresponding to the predicted labels, returned as a numeric vector. Each entry of cnum corresponds to the predicted class number for the corresponding row of X.

More About

Predicted Class Label

predict classifies by minimizing the expected misclassification cost:

$$\widehat{y}=\underset{y=1,\mathrm{...},K}{\arg \min }{\displaystyle \sum _{j=1}^K\widehat{P}\left(j|x\right)C\left(y|j\right)},$$

where:

• $$\widehat{y}$$ is the predicted classification.

• K is the number of classes.

• $$\widehat{P}\left(j|x\right)$$ is the posterior probability of class j for observation x.

• $$C\left(y|j\right)$$ is the cost of classifying an observation as y when its true class is j.

Score (tree)

For trees, the score of a classification of a leaf node is the posterior probability of the classification at that node. The posterior probability of the classification at a node is the number of training sequences that lead to that node with the classification, divided by the number of training sequences that lead to that node.

For an example, see Posterior Probability Definition for Classification Tree.

True Misclassification Cost

The true misclassification cost is the cost of classifying an observation into an incorrect class.

You can set the true misclassification cost per class by using the Cost name-value argument when you create the classifier. Cost(i,j) is the cost of classifying an observation into class j when its true class is i. By default, Cost(i,j)=1 if i~=j, and Cost(i,j)=0 if i=j. In other words, the cost is 0 for correct classification and 1 for incorrect classification.

Expected Cost

The expected misclassification cost per observation is an averaged cost of classifying the observation into each class.

Suppose you have Nobs observations that you want to classify with a trained classifier, and you have K classes. You place the observations into a matrix X with one observation per row.

The expected cost matrix CE has size Nobs-by-K. Each row of CE contains the expected (average) cost of classifying the observation into each of the K classes. CE(n,k) is

$${\displaystyle \sum _{i=1}^K\widehat{P}\left(i|X(n)\right)C\left(k|i\right)},$$

where:

• K is the number of classes.

• $$\widehat{P}\left(i|X(n)\right)$$ is the posterior probability of class i for observation X(n).

• $$C\left(k|i\right)$$ is the true misclassification cost of classifying an observation as k when its true class is i.

Predictive Measure of Association

The predictive measure of association is a value that indicates the similarity between decision rules that split observations. Among all possible decision splits that are compared to the optimal split (found by growing the tree), the best surrogate decision split yields the maximum predictive measure of association. The second-best surrogate split has the second-largest predictive measure of association.

Suppose x_j and x_k are predictor variables j and k, respectively, and j ≠ k. At node t, the predictive measure of association between the optimal split x_j < u and a surrogate split x_k < v is

$${\lambda }_{jk}=\frac{\text{min}\left(P_L,P_R\right)-\left(1-P_{L_j{L}_k}-P_{R_j{R}_k}\right)}{\text{min}\left(P_L,P_R\right)}.$$

• P_L is the proportion of observations in node t, such that x_j < u. The subscript L stands for the left child of node t.

• P_R is the proportion of observations in node t, such that x_j ≥ u. The subscript R stands for the right child of node t.

• $$P_{L_j{L}_k}$$ is the proportion of observations at node t, such that x_j < u and x_k < v.

• $$P_{R_j{R}_k}$$ is the proportion of observations at node t, such that x_j ≥ u and x_k ≥ v.

• Observations with missing values for x_j or x_k do not contribute to the proportion calculations.

λ_jk is a value in (–∞,1]. If λ_jk > 0, then x_k < v is a worthwhile surrogate split for x_j < u.

Algorithms

predict generates predictions by following the branches of tree until it reaches a leaf node or a missing value. If predict reaches a leaf node, it returns the classification of that node.

If predict reaches a node with a missing value for a predictor, its behavior depends on the setting of the Surrogate name-value argument when fitctree constructs tree.

• Surrogate = "off" (default) — predict returns the label with the largest number of training samples that reach the node.

• Surrogate = "on" — predict uses the best surrogate split at the node. If all surrogate split variables with positive predictive measure of association are missing, predict returns the label with the largest number of training samples that reach the node. For a definition, see Predictive Measure of Association.

Alternative Functionality

Simulink Block

To integrate the prediction of a classification tree model into Simulink^®, you can use the ClassificationTree Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB^® Function block with the predict function. For examples, see Predict Class Labels Using ClassificationTree 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 CompactClassificationTree 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).
  label	If the response data type is char and codegen cannot determine that subtrees is a scalar, then label is a cell array of character vectors.
  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