# fitctree

Fit binary classification decision tree for multiclass classification

## Syntax

• ``tree = fitctree(TBL,ResponseVarName)``
• ``tree = fitctree(TBL,formula)``
• ``tree = fitctree(TBL,Y)``
• ``tree = fitctree(X,Y)``
example
• ``tree = fitctree(___,Name,Value)``
example

## Description

````tree = fitctree(TBL,ResponseVarName)` returns a fitted binary classification decision tree based on the input variables (also known as predictors, features, or attributes) contained in the table `TBL` and output (response or labels) contained in `ResponseVarName`. The returned binary tree splits branching nodes based on the values of a column of `TBL`.```
````tree = fitctree(TBL,formula)` returns a fitted binary classification decision tree based on the input variables contained in the table `TBL`. `formula` is a formula string that identifies the response and predictor variables in `TBL` used to fit `tree`. The returned binary tree splits branching nodes based on the values of a column of `TBL`.```
````tree = fitctree(TBL,Y)` returns a fitted binary classification decision tree based on the input variables contained in the table `TBL` and output in vector `Y`. The returned binary tree splits branching nodes based on the values of a column of `TBL`.```

example

````tree = fitctree(X,Y)` returns a fitted binary classification decision tree based on the input variables contained in matrix `X` and output `Y`. The returned binary tree splits branching nodes based on the values of a column of `X`.```

example

````tree = fitctree(___,Name,Value)` fits a tree with additional options specified by one or more name-value pair arguments, using any of the previous syntaxes. For example, you can specify the algorithm used to find the best split on a categorical predictor, grow a cross-validated tree, or hold out a fraction of the input data for validation.```

## Examples

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### Grow a Classification Tree

Grow a classification tree using the `ionosphere` data set.

```load ionosphere tc = fitctree(X,Y) ```
```tc = ClassificationTree PredictorNames: {1x34 cell} ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'b' 'g'} ScoreTransform: 'none' NumObservations: 351 ```

### Control Tree Depth

You can control the depth of the trees using the `MaxNumSplits`, `MinLeafSize`, or `MinParentSize` name-value pair parameters. `fitctree` grows deep decision trees by default. You can grow shallower trees to reduce model complexity or computation time.

Load the `ionosphere` data set.

```load ionosphere ```

The default values of the tree depth controllers for growing classification trees are:

• `n - 1` for `MaxNumSplits`. `n` is the training sample size.

• `1` for `MinLeafSize`.

• `10` for `MinParentSize`.

These default values tend to grow deep trees for large training sample sizes.

Train a classification tree using the default values for tree depth control. Cross validate the model using 10-fold cross validation.

```rng(1); % For reproducibility MdlDefault = fitctree(X,Y,'CrossVal','on'); ```

Draw a histogram of the number of imposed splits on the trees. Also, view one of the trees.

```numBranches = @(x)sum(x.IsBranch); mdlDefaultNumSplits = cellfun(numBranches, MdlDefault.Trained); figure; histogram(mdlDefaultNumSplits) view(MdlDefault.Trained{1},'Mode','graph') ```

The average number of splits is around 15.

Suppose that you want a classification tree that is not as complex (deep) as the ones trained using the default number of splits. Train another classification tree, but set the maximum number of splits at 7, which is about half the mean number of splits from the default classification tree. Cross validate the model using 10-fold cross validation.

```Mdl7 = fitctree(X,Y,'MaxNumSplits',7,'CrossVal','on'); view(Mdl7.Trained{1},'Mode','graph') ```

Compare the cross validation classification errors of the models.

```classErrorDefault = kfoldLoss(MdlDefault) classError7 = kfoldLoss(Mdl7) ```
```classErrorDefault = 0.1140 classError7 = 0.1254 ```

`Mdl7` is much less complex and performs only slightly worse than `MdlDefault`.

## Input Arguments

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### `TBL` — Sample datatable

Sample data used to train the model, specified as a table. Each row of `TBL` corresponds to one observation, and each column corresponds to one predictor variable. Optionally, `TBL` can contain one additional column for the response variable. Multi-column variables and cell arrays other than cell arrays of strings are not allowed.

If `TBL` contains the response variable, and you want to use all remaining variables in `TBL` as predictors, then specify the response variable using `ResponseVarName`.

If `TBL` contains the response variable, and you want to use only a subset of the remaining variables in `TBL` as predictors, then specify a formula string using `formula`.

If `TBL` does not contain the response variable, then specify a response variable using `Y`. The length of response variable and the number of rows of `TBL` must be equal.

Data Types: `table`

### `X` — Predictor valuesmatrix of floating-point values

Predictor values, specified as a matrix of floating-point values.

`fitctree` considers `NaN` values in `X` as missing values. `fitctree` does not use observations with all missing values for `X` in the fit. `fitctree` uses observations with some missing values for `X` to find splits on variables for which these observations have valid values.

Data Types: `single` | `double`

### `ResponseVarName` — Response variable namename of a variable in `TBL`

Response variable name, specified as the name of a variable in `TBL`.

You must specify `ResponseVarName` as a string. For example, if the response variable `Y` is stored as `TBL.Y`, then specify it as `'Y'`. Otherwise, the software treats all columns of `TBL`, including `Y`, as predictors when training the model.

The response variable must be a categorical or character array, logical or numeric vector, or cell array of strings. If `Y` is a character array, then each element must correspond to one row of the array.

It is good practice to specify the order of the classes using the `ClassNames` name-value pair argument.

### `formula` — Response and predictor variables to use in model trainingstring in the form of `'Y~X1+X2+X3'`

Response and predictor variables to use in model training, specified as a string in the form of `'Y~X1+X2+X3'`. In this form, `Y` represents the response variable, and `X1`, `X2`, and `X3` represent the predictor variables.

To specify a subset of variables in `TBL` as predictors for training the model, use a formula string. If you specify a formula string, then any variables in `TBL` that do not appear in `formula` are not used to train the model.

### `Y` — Class labelsnumeric vector | categorical vector | logical vector | character array | cell array of strings

Class labels, specified as a numeric vector, categorical vector, logical vector, character array, or cell array of strings. Each row of `X` represents the classification of the corresponding row of `X`.

When fitting the tree, `fitctree` considers `NaN`, `''` (empty string), and `<undefined>` values in `Y` to be missing values. `fitctree` does not use observations with missing values for `Y` in the fit.

For numeric `Y`, consider fitting a regression tree using `fitrtree` instead.

Data Types: `single` | `double` | `char` | `logical` | `cell`

### Name-Value Pair Arguments

Specify optional comma-separated pairs of `Name,Value` arguments. `Name` is the argument name and `Value` is the corresponding value. `Name` must appear inside single quotes (`' '`). You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.

Example: `'CrossVal','on','MinLeafSize',40` specifies a cross-validated classification tree with a minimum of 40 observations per leaf.

### `'AlgorithmForCategorical'` — Algorithm for best categorical predictor split`'Exact'` | `'PullLeft'` | `'PCA'` | `'OVAbyClass'`

Algorithm to find the best split on a categorical predictor with C categories for data and K ≥ 3 classes, specified as the comma-separated pair consisting of `'AlgorithmForCategorical'` and one of the following.

 `'Exact'` Consider all 2C–1 – 1 combinations. `'PullLeft'` Start with all C categories on the right branch. Consider moving each category to the left branch as it achieves the minimum impurity for the K classes among the remaining categories. From this sequence, choose the split that has the lowest impurity. `'PCA'` Compute a score for each category using the inner product between the first principal component of a weighted covariance matrix (of the centered class probability matrix) and the vector of class probabilities for that category. Sort the scores in ascending order, and consider all C – 1 splits. `'OVAbyClass'` Start with all C categories on the right branch. For each class, order the categories based on their probability for that class. For the first class, consider moving each category to the left branch in order, recording the impurity criterion at each move. Repeat for the remaining classes. From this sequence, choose the split that has the minimum impurity.

`fitctree` automatically selects the optimal subset of algorithms for each split using the known number of classes and levels of a categorical predictor. For K = 2 classes, `fitctree` always performs the exact search. To specify a particular algorithm, use the `'AlgorithmForCategorical'` name-value pair argument.

Example: `'AlgorithmForCategorical','PCA'`

### `'CategoricalPredictors'` — Categorical predictors listnumeric or logical vector | cell array of strings | character matrix | `'all'`

Categorical predictors list, specified as the comma-separated pair consisting of `'CategoricalPredictors'` and one of the following:

• A numeric vector with indices from `1` through `p`, where `p` is the number of columns of `X`.

• A logical vector of length `p`, where a `true` entry means that the corresponding column of `X` is a categorical variable.

• A cell array of strings, where each element in the array is the name of a predictor variable. The names must match entries in `PredictorNames` values.

• A character matrix, where each row of the matrix is a name of a predictor variable. The names must match entries in `PredictorNames` values. Pad the names with extra blanks so each row of the character matrix has the same length.

• `'all'`, meaning all predictors are categorical.

By default, if the predictor data is in a matrix (`X`), the software assumes that none of the predictors are categorical. If the predictor data is in a table (`TBL`), the software assumes that a variable is categorical if it contains, logical values, values of the unordered data type `categorical`, or a cell array of strings.

Example: `'CategoricalPredictors','all'`

Data Types: `single` | `double` | `char`

### `'ClassNames'` — Class namesnumeric vector | categorical vector | logical vector | character array | cell array of strings

Class names, specified as the comma-separated pair consisting of `'ClassNames'` and an array representing the class names. Use the same data type as the values that exist in `Y`.

To order the classes or to select a subset of classes for training, use `ClassNames`. The default is the class names that exist in `Y`.

Data Types: `single` | `double` | `char` | `logical` | `cell`

### `'Cost'` — Cost of misclassificationsquare matrix | structure

Cost of misclassification of a point, specified as the comma-separated pair consisting of `'Cost'` and one of the following:

• Square matrix, where `Cost(i,j)` is the cost of classifying a point into class `j` if its true class is `i` (i.e., the rows correspond to the true class and the columns correspond to the predicted class). To specify the class order for the corresponding rows and columns of `Cost`, also specify the `ClassNames` name-value pair argument.

• Structure `S` having two fields: `S.ClassNames` containing the group names as a variable of the same data type as `Y`, and `S.ClassificationCosts` containing the cost matrix.

The default is `Cost(i,j)=1` if `i~=j`, and `Cost(i,j)=0` if `i=j`.

Data Types: `single` | `double` | `struct`

### `'CrossVal'` — Flag to grow cross-validated decision tree`'off'` (default) | `'on'`

Flag to grow a cross-validated decision tree, specified as the comma-separated pair consisting of `'CrossVal'` and `'on'` or `'off'`.

If `'on'`, `fitctree` grows a cross-validated decision tree with 10 folds. You can override this cross-validation setting using one of the `'KFold'`, `'Holdout'`, `'Leaveout'`, or `'CVPartition'` name-value pair arguments. You can only use one of these four arguments at a time when creating a cross-validated tree.

Alternatively, cross validate `tree` later using the `crossval` method.

Example: `'CrossVal','on'`

### `'CVPartition'` — Partition for cross-validated tree`cvpartition` object

Partition to use in a cross-validated tree, specified as the comma-separated pair consisting of `'CVPartition'` and an object created using `cvpartition`.

If you use `'CVPartition'`, you cannot use any of the `'KFold'`, `'Holdout'`, or `'Leaveout'` name-value pair arguments.

### `'Holdout'` — Fraction of data for holdout validation`0` (default) | scalar value in the range `[0,1]`

Fraction of data used for holdout validation, specified as the comma-separated pair consisting of `'Holdout'` and a scalar value in the range `[0,1]`. Holdout validation tests the specified fraction of the data, and uses the rest of the data for training.

If you use `'Holdout'`, you cannot use any of the `'CVPartition'`, `'KFold'`, or `'Leaveout'` name-value pair arguments.

Example: `'Holdout',0.1`

Data Types: `single` | `double`

### `'KFold'` — Number of folds`10` (default) | positive integer value

Number of folds to use in a cross-validated tree, specified as the comma-separated pair consisting of `'KFold'` and a positive integer value. `KFold` must be greater than 1.

If you use `'KFold'`, you cannot use any of the `'CVPartition'`, `'Holdout'`, or `'Leaveout'` name-value pair arguments.

Example: `'KFold',8`

Data Types: `single` | `double`

### `'Leaveout'` — Leave-one-out cross-validation flag`'off'` (default) | `'on'`

Leave-one-out cross-validation flag, specified as the comma-separated pair consisting of `'Leaveout'` and `'on'` or `'off'`. Specify `'on'` to use leave-one-out cross-validation.

If you use `'Leaveout'`, you cannot use any of the `'CVPartition'`, `'Holdout'`, or `'KFold'` name-value pair arguments.

Example: `'Leaveout','on'`

### `'MaxNumCategories'` — Maximum category levels`10` (default) | nonnegative scalar value

Maximum category levels, specified as the comma-separated pair consisting of `'MaxNumCategories'` and a nonnegative scalar value. `fitctree` splits a categorical predictor using the exact search algorithm if the predictor has at most `MaxNumCategories` levels in the split node. Otherwise, `fitctree` finds the best categorical split using one of the inexact algorithms.

Passing a small value can lead to loss of accuracy and passing a large value can increase computation time and memory overload.

Example: `'MaxNumCategories',8`

### `'MaxNumSplits'` — Maximal number of decision splits`size(X,1) - 1` (default) | positive integer

Maximal number of decision splits (or branch nodes), specified as the comma-separated pair consisting of `'MaxNumSplits'` and a positive integer. `fitctree` splits `MaxNumSplits` or fewer branch nodes. For more details on splitting behavior, see Algorithms.

Example: `'MaxNumSplits',5`

Data Types: `single` | `double`

### `'MergeLeaves'` — Leaf merge flag`'on'` (default) | `'off'`

Leaf merge flag, specified as the comma-separated pair consisting of `'MergeLeaves'` and `'on'` or `'off'`.

If `MergeLeaves` is `'on'`, then `fitctree`:

• Merges leaves that originate from the same parent node, and that yields a sum of risk values greater or equal to the risk associated with the parent node

• Estimates the optimal sequence of pruned subtrees, but does not prune the classification tree

Otherwise, `fitctree` does not merge leaves.

Example: `'MergeLeaves','off'`

### `'MinLeafSize'` — Minimum number of leaf node observations`1` (default) | positive integer value

Minimum number of leaf node observations, specified as the comma-separated pair consisting of `'MinLeafSize'` and a positive integer value. Each leaf has at least `MinLeafSize` observations per tree leaf. If you supply both `MinParentSize` and `MinLeafSize`, `fitctree` uses the setting that gives larger leaves: `MinParentSize = max(MinParentSize,2*MinLeafSize)`.

Example: `'MinLeafSize',3`

Data Types: `single` | `double`

### `'MinParentSize'` — Minimum number of branch node observations`10` (default) | positive integer value

Minimum number of branch node observations, specified as the comma-separated pair consisting of `'MinParentSize'` and a positive integer value. Each branch node in the tree has at least `MinParentSize` observations. If you supply both `MinParentSize` and `MinLeafSize`, `fitctree` uses the setting that gives larger leaves: `MinParentSize = max(MinParentSize,2*MinLeafSize)`.

Example: `'MinParentSize',8`

Data Types: `single` | `double`

### `'NumVariablesToSample'` — Number of predictors to select at random for each split`'all'` | positive integer value

Number of predictors to select at random for each split, specified as the comma-separated pair consisting of `'NumVariablesToSample'` and a positive integer value. You can also specify `'all'` to use all available predictors.

Example: `'NumVariablesToSample',3`

Data Types: `single` | `double`

### `'PredictorNames'` — Predictor variable names`{'x1','x2',...}` (default) | cell array of strings

Predictor variable names, specified as the comma-separated pair consisting of `'PredictorNames'` and a cell array of strings containing the names for the predictor variables, in the order in which they appear in `X` or `TBL`.

If you specify the predictors as a table (`TBL`), `PredictorNames` must be a subset of the variable names in `TBL`. In this case, the software uses only the variables in `PredictorNames` to fit the model. If you use formula to specify the model, then you cannot use the `PredictorNames` name-value pair.

Example: `'PredictorNames',{'PetalWidth','PetalLength'}`

### `'Prior'` — Prior probabilities`'empirical'` (default) | `'uniform'` | vector of scalar values | structure

Prior probabilities for each class, specified as the comma-separated pair consisting of `'Prior'` and one of the following.

• A string:

• `'empirical'` determines class probabilities from class frequencies in `Y`. If you pass observation weights, `fitctree` uses the weights to compute the class probabilities.

• `'uniform'` sets all class probabilities equal.

• A vector (one scalar value for each class). To specify the class order for the corresponding elements of `Prior`, also specify the `ClassNames` name-value pair argument.

• A structure `S` with two fields:

• `S.ClassNames` containing the class names as a variable of the same type as `Y`

• `S.ClassProbs` containing a vector of corresponding probabilities

If you set values for both `weights` and `prior`, the weights are renormalized to add up to the value of the prior probability in the respective class.

Example: `'Prior','uniform'`

### `'Prune'` — Flag to estimate optimal sequence of pruned subtrees`'on'` (default) | `'off'`

Flag to estimate the optimal sequence of pruned subtrees, specified as the comma-separated pair consisting of `'Prune'` and `'on'` or `'off'`.

If `Prune` is `'on'`, then `fitctree` grows the classification tree without pruning it, but estimates the optimal sequence of pruned subtrees. Otherwise, `fitctree` grows the classification tree without estimating the optimal sequence of pruned subtrees.

To prune a trained `ClassificationTree` model, pass it to `prune`.

Example: `'Prune','off'`

### `'PruneCriterion'` — Pruning criterion`'error'` (default) | `'impurity'`

Pruning criterion, specified as the comma-separated pair consisting of `'PruneCriterion'` and `'error'` or `'impurity'`.

Example: `'PruneCriterion','impurity'`

### `'ResponseName'` — Response variable name`'Y'` (default) | string

Response variable name, specified as the comma-separated pair consisting of `'ResponseName'` and a string representing the name of the response variable.

This name-value pair is not valid when using the `ResponseVarName` or `formula` input arguments.

Example: `'ResponseName','IrisType'`

### `'ScoreTransform'` — Score transform function`'none'` | `'symmetric'` | `'invlogit'` | `'ismax'` | function handle | ...

Score transform function, specified as the comma-separated pair consisting of `'ScoreTransform'` and a function handle for transforming scores. Your function must accept a matrix (the original scores) and return a matrix of the same size (the transformed scores).

Alternatively, you can specify one of the following strings representing a built-in transformation function.

StringFormula
`'doublelogit'`1/(1 + e–2x)
`'invlogit'`log(x / (1–x))
`'ismax'`Set the score for the class with the largest score to `1`, and scores for all other classes to `0`.
`'logit'`1/(1 + ex)
`'none'`x (no transformation)
`'sign'`–1 for x < 0
0 for x = 0
1 for x > 0
`'symmetric'`2x – 1
`'symmetriclogit'`2/(1 + ex) – 1
`'symmetricismax'`Set the score for the class with the largest score to `1`, and scores for all other classes to `-1`.

Example: `'ScoreTransform','logit'`

### `'SplitCriterion'` — Split criterion`'gdi'` (default) | `'twoing'` | `'deviance'`

Split criterion, specified as the comma-separated pair consisting of `'SplitCriterion'` and `'gdi'` (Gini's diversity index), `'twoing'` for the twoing rule, or `'deviance'` for maximum deviance reduction (also known as cross entropy).

Example: `'SplitCriterion','deviance'`

### `'Surrogate'` — Surrogate decision splits flag`'off'` | `'on'` | `'all'` | positive integer value

Surrogate decision splits flag, specified as the comma-separated pair consisting of `'Surrogate'` and `'on'`, `'off'`, `'all'`, or a positive integer value.

• When set to `'on'`, `fitctree` finds at most 10 surrogate splits at each branch node.

• When set to `'all'`, `fitctree` finds all surrogate splits at each branch node. The `'all'` setting can use considerable time and memory.

• When set to a positive integer value, `fitctree` finds at most the specified number of surrogate splits at each branch node.

Use surrogate splits to improve the accuracy of predictions for data with missing values. The setting also lets you compute measures of predictive association between predictors. For more details, see Node Splitting Rules.

Example: `'Surrogate','on'`

### `'Weights'` — Observation weights`ones(size(x,1),1)` (default) | vector of scalar values

Observation weights, specified as the comma-separated pair consisting of `'Weights'` and a vector of scalar values. The software weights the observations in each row of `X` or `TBL` with the corresponding value in `Weights`. The size of `Weights` must equal the number of rows in `X` or `TBL`.

If you specify the input data as a table `TBL`, then `Weights` can be the name of a variable in `TBL` that contains a numeric vector. In this case, you must specify `Weights` as a variable name string. For example, if weights vector `W` is stored as `TBL.W`, then specify it as `'W'`. Otherwise, the software treats all columns of `TBL`, including `W`, as predictors when training the model.

`fitctree` normalizes the weights in each class to add up to the value of the prior probability of the class.

Data Types: `single` | `double`

## Output Arguments

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### `tree` — Classification treeclassification tree object

Classification tree, returned as a classification tree object.

Using the `'CrossVal'`, `'KFold'`, `'Holdout'`, `'Leaveout'`, or `'CVPartition'` options results in a tree of class `ClassificationPartitionedModel`. You cannot use a partitioned tree for prediction, so this kind of tree does not have a `predict` method. Instead, use `kfoldpredict` to predict responses for observations not used for training.

Otherwise, `tree` is of class `ClassificationTree`, and you can use the `predict` method to make predictions.

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### Impurity and Node Error

`ClassificationTree` splits nodes based on either impurity or node error.

Impurity means one of several things, depending on your choice of the `SplitCriterion` name-value pair argument:

• Gini's Diversity Index (`gdi`) — The Gini index of a node is

$1-\sum _{i}{p}^{2}\left(i\right),$

where the sum is over the classes i at the node, and p(i) is the observed fraction of classes with class i that reach the node. A node with just one class (a pure node) has Gini index `0`; otherwise the Gini index is positive. So the Gini index is a measure of node impurity.

• Deviance (`'deviance'`) — With p(i) defined the same as for the Gini index, the deviance of a node is

$-\sum _{i}p\left(i\right)\mathrm{log}p\left(i\right).$

A pure node has deviance `0`; otherwise, the deviance is positive.

• Twoing rule (`'twoing'`) — Twoing is not a purity measure of a node, but is a different measure for deciding how to split a node. Let L(i) denote the fraction of members of class i in the left child node after a split, and R(i) denote the fraction of members of class i in the right child node after a split. Choose the split criterion to maximize

$P\left(L\right)P\left(R\right){\left(\sum _{i}|L\left(i\right)-R\left(i\right)|\right)}^{2},$

where P(L) and P(R) are the fractions of observations that split to the left and right respectively. If the expression is large, the split made each child node purer. Similarly, if the expression is small, the split made each child node similar to each other, and hence similar to the parent node, and so the split did not increase node purity.

• Node error — The node error is the fraction of misclassified classes at a node. If j is the class with the largest number of training samples at a node, the node error is

1 – p(j).

### 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.

At node t, the predictive measure of association between the optimal split xj < sj and a surrogate split xk < sk, jk, is

${\lambda }_{jk}=\frac{\text{min}\left({P}_{L},{P}_{R}\right)-\left(1-{P}_{{L}_{j}{L}_{k}}-{P}_{{R}_{j}{R}_{k}}\right)}{\text{min}\left({P}_{L},{P}_{R}\right)}.$

• PL is the proportion of observations in node t, such that xj < sj. The subscript L stands for the left child of node t.

• PR is the proportion of observations in node t, such that xjsj. 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 xj < sj and xk < sk.

• ${P}_{{R}_{j}{R}_{k}}$ is the proportion of observations at node t, such that xjsj and xksk.

• Observations with missing values for xj or xk do not contribute to the proportion calculations.

λjk is a value in (–∞,1]. If λjk > 0, then xk is a worthwhile surrogate split for xj.

### Surrogate Decision Splits

A surrogate decision split is an alternative to the optimal decision split at a given node in a decision tree. The optimal split is found by growing the tree; the surrogate split uses a similar or correlated predictor variable and split criterion.

When the value of the optimal split predictor for an observation is missing, the observation is sent to the left or right child node using the best surrogate predictor. When the value of the best surrogate split predictor for the observation is also missing, the observation is sent to the left or right child node using the second-best surrogate predictor, and so on. Candidate splits are sorted in descending order by their predictive measure of association.

Suppose that the optimal splitting criterion at node t is xj < sj. The is a value that determines the similarity between the optimal split xj < sj and a split using another predictor. Candidate splits are sorted in descending order by their predictive measure of association. The best surrogate split xk < sk, kj, has the largest predictive measure of association with the optimal split among all other decision splits at node t. The second best surrogate split has the second largest predictive measure of association, and so on.

### Tips

By default, `Prune` is `'on'`. However, this specification does not prune the classification tree. To prune a trained classification tree, pass the classification tree to `prune`.

### Node Splitting Rules

`fitctree` follows these steps to determine how to split node t. For all predictors xi, i = 1,...,p:

1. `fitctree` computes the weighted impurity of node t, it. For supported impurity measures, see `SplitCriterion`.

2. `fitctree` estimates the probability that an observation is in node t using

$P\left(T\right)=\sum _{j\in T}{w}_{j}.$

wj is the weight of observation j, and T is the set of all observation indices in node t. If you do not specify `Prior` or `Weights`, then wj = 1/n, where n is the sample size.

3. `fitctree` sorts xi in ascending order. Each element of the sorted predictor is a splitting candidate or cut point. `fitctree` stores any indices corresponding to missing values in the set TU, which is the unsplit set.

4. `fitctree` determines the best way to split node t using xi by maximizing the impurity gain (ΔI) over all splitting candidates. That is, for all splitting candidates in xi:

1. `fitctree` splits the observations in node t into left and right child nodes (tL and tR, respectively).

2. `fitctree` computes ΔI. Suppose that for a particular splitting candidate, tL and tR contain observation indices in the sets TL and TR, respectively.

• If xi does not contain any missing values, then the impurity gain for the current splitting candidate is

$\Delta I=P\left(T\right){i}_{t}-P\left({T}_{L}\right){i}_{{t}_{L}}-P\left({T}_{R}\right){i}_{{t}_{R}}.$

• If xi contains missing values then, assuming that the observations are missing at random, the impurity gain is

$\Delta {I}_{U}=P\left(T-{T}_{U}\right){i}_{t}-P\left({T}_{L}\right){i}_{{t}_{L}}-P\left({T}_{R}\right){i}_{{t}_{R}}.$

TTU is the set of all observation indices in node t that are not missing.

• If you use surrogate decision splits, then:

1. `fitctree` computes the predictive measures of association between the decision split xi < si and all possible decision splits xk < sk, ki.

2. `fitctree` sorts the possible alternative decision splits in descending order by their predictive measure of association with the optimal split. The surrogate split is the decision split yielding the largest measure.

3. `fitctree` decides the child node assignments for observations with a missing value for xi using the surrogate split. If the surrogate predictor also contains a missing value, then `fitctree` uses the decision split with the second largest measure, and so on, until there are no other surrogates. It is possible for `fitctree` to split two different observations at node t using two different surrogate splits. For example, suppose the predictors x1 and x2 are the best and second best surrogates, respectively, for the predictor xi, i ∉ {1,2}, at node t. If observation m of predictor xi is missing (i.e., xmi is missing), but xm1 is not missing, then x1 is the surrogate predictor for observation xmi. If observations x(m + 1),i and x(m + 1),1 are missing, but x(m + 1),2 is not missing, then x2 is the surrogate predictor for observation m + 1.

4. `fitctree` uses the appropriate impurity gain formula. That is, if `fitctree` fails to assign all missing observations in node t to children nodes using surrogate splits, then the impurity gain is ΔIU. Otherwise, `fitctree` uses ΔI for the impurity gain.

3. `fitctree` chooses the candidate that yields the largest impurity gain.

`fitctree` splits the predictor variable at the cut point that maximizes the impurity gain.

### Tree Depth Control

• If `MergeLeaves` is `'on'` and `PruneCriterion` is `'error'` (which are the default values for these name-value pair arguments), then the software applies pruning only to the leaves and by using classification error. This specification amounts to merging leaves that share the most popular class per leaf.

• To accommodate `MaxNumSplits`, `fitctree` splits all nodes in the current layer, and then counts the number of branch nodes. A layer is the set of nodes that are equidistant from the root node. If the number of branch nodes exceeds `MaxNumSplits`, `fitctree` follows this procedure:

1. Determine how many branch nodes in the current layer must be unsplit so that there are at most `MaxNumSplits` branch nodes.

2. Sort the branch nodes by their impurity gains.

3. Unsplit the number of least successful branches.

4. Return the decision tree grown so far.

This procedure produces maximally balanced trees.

• The software splits branch nodes layer by layer until at least one of these events occurs:

• There are `MaxNumSplits` branch nodes.

• A proposed split causes the number of observations in at least one branch node to be fewer than `MinParentSize`.

• A proposed split causes the number of observations in at least one leaf node to be fewer than `MinLeafSize`.

• The algorithm cannot find a good split within a layer (i.e., the pruning criterion (see `PruneCriterion`), does not improve for all proposed splits in a layer). A special case is when all nodes are pure (i.e., all observations in the node have the same class).

`MaxNumSplits` and `MinLeafSize` do not affect splitting at their default values. Therefore, if you set `'MaxNumSplits'`, splitting might stop due to the value of `MinParentSize`, before `MaxNumSplits` splits occur.

### Parallelization

For dual-core systems and above, `fitctree` parallelizes training decision trees using Intel® Threading Building Blocks (TBB). For details on Intel TBB, see https://software.intel.com/en-us/intel-tbb.

## References

[1] Coppersmith, D., S. J. Hong, and J. R. M. Hosking. "Partitioning Nominal Attributes in Decision Trees." Data Mining and Knowledge Discovery, Vol. 3, 1999, pp. 197–217.

[2] Breiman, L., J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Boca Raton, FL: CRC Press, 1984.