# templateECOC

Error-correcting output codes learner template

## Syntax

``t = templateECOC()``
``t = templateECOC(Name,Value)``

## Description

example

````t = templateECOC()` returns an error-correcting output codes (ECOC) classification learner template.If you specify a default template, then the software uses default values for all input arguments during training.```

example

````t = templateECOC(Name,Value)` returns a template with additional options specified by one or more name-value pair arguments.For example, you can specify a coding design, whether to fit posterior probabilities, or the types of binary learners.If you display `t` in the Command Window, then all options appear empty (`[]`), except those that you specify using name-value pair arguments. During training, the software uses default values for empty options.```

## Examples

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Use `templateECOC` to create a default ECOC template.

`t = templateECOC()`
```t = Fit template for classification ECOC. BinaryLearners: '' Coding: '' FitPosterior: [] Options: [] VerbosityLevel: [] NumConcurrent: [] Version: 1 Method: 'ECOC' Type: 'classification' ```

All properties of the template object are empty except for `Method` and `Type`. When you pass `t` to `testckfold`, the software fills in the empty properties with their respective default values. For example, the software fills the `BinaryLearners` property with `'SVM'`. For details on other default values, see `fitcecoc`.

`t` is a plan for an ECOC learner. When you create it, no computation occurs. You can pass `t` to `testckfold` to specify a plan for an ECOC classification model to statistically compare with another model.

One way to select predictors or features is to train two models where one that uses a subset of the predictors that trained the other. Statistically compare the predictive performances of the models. If there is sufficient evidence that model trained on fewer predictors performs better than the model trained using more of the predictors, then you can proceed with a more efficient model.

Load Fisher's iris data set. Plot all 2-dimensional combinations of predictors.

```load fisheriris d = size(meas,2); % Number of predictors pairs = nchoosek(1:d,2)```
```pairs = 6×2 1 2 1 3 1 4 2 3 2 4 3 4 ```
```for j = 1:size(pairs,1) subplot(3,2,j) gscatter(meas(:,pairs(j,1)),meas(:,pairs(j,2)),species) xlabel(sprintf('meas(:,%d)',pairs(j,1))) ylabel(sprintf('meas(:,%d)',pairs(j,2))) legend off end```

Based on the scatterplot, `meas(:,3)` and `meas(:,4)` seem like they separate the groups well.

Create an ECOC template. Specify to use a one-versus-all coding design.

`t = templateECOC('Coding','onevsall');`

By default, the ECOC model uses linear SVM binary learners. You can choose other, supported algorithms by specifying them using the `'Learners'` name-value pair argument.

Test whether an ECOC model that is just trained using predictors 3 and 4 performs at most as well as an ECOC model that is trained using all predictors. Rejecting this null hypothesis means that the ECOC model trained using predictors 3 and 4 performs better than the ECOC model trained using all predictors. Suppose ${C}_{1}$ represents the classification error of the ECOC model trained using predictors 3 and 4 and ${C}_{2}$ represents the classification error of the ECOC model trained using all predictors, then the test is:

`$\begin{array}{l}{H}_{0}:{C}_{1}\ge {C}_{2}\\ {H}_{1}:{C}_{1}<{C}_{2}\end{array}$`

By default, `testckfold` conducts a 5-by-2 k-fold F test, which is not appropriate as a one-tailed test. Specify to conduct a 5-by-2 k-fold t test.

```rng(1); % For reproducibility [h,pValue] = testckfold(t,t,meas(:,pairs(6,:)),meas,species,... 'Alternative','greater','Test','5x2t')```
```h = logical 0 ```
```pValue = 0.8940 ```

The `h = 0` indicates that there is not enough evidence to suggest that the model trained using predictors 3 and 4 is more accurate than the model trained using all predictors.

## Input Arguments

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### Name-Value 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 quotes. You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.

Example: `'Coding','ternarycomplete','FitPosterior',true,'Learners','tree'` specifies a ternary complete coding design, to transform scores to posterior probabilities, and to grow classification trees for all binary learners.

Coding design name, specified as the comma-separated pair consisting of `'Coding'` and a numeric matrix or a value in this table.

ValueNumber of Binary LearnersDescription
`'allpairs'` and `'onevsone'`K(K – 1)/2For each binary learner, one class is positive, another is negative, and the software ignores the rest. This design exhausts all combinations of class pair assignments.
`'binarycomplete'`${2}^{\left(K-1\right)}-1$This design partitions the classes into all binary combinations, and does not ignore any classes. For each binary learner, all class assignments are `-1` and `1` with at least one positive and negative class in the assignment.
`'denserandom'`Random, but approximately 10 log2KFor each binary learner, the software randomly assigns classes into positive or negative classes, with at least one of each type. For more details, see Random Coding Design Matrices.
`'onevsall'`KFor each binary learner, one class is positive and the rest are negative. This design exhausts all combinations of positive class assignments.
`'ordinal'`K – 1For the first binary learner, the first class is negative, and the rest positive. For the second binary learner, the first two classes are negative, the rest positive, and so on.
`'sparserandom'`Random, but approximately 15 log2KFor each binary learner, the software randomly assigns classes as positive or negative with probability 0.25 for each, and ignores classes with probability 0.5. For more details, see Random Coding Design Matrices.
`'ternarycomplete'`$\left({3}^{K}-{2}^{\left(K+1\right)}+1\right)/2$This design partitions the classes into all ternary combinations. All class assignments are `0`, `-1`, and `1` with at least one positive and one negative class in the assignment.

You can also specify a coding design using a custom coding matrix. The custom coding matrix is a K-by-L matrix. Each row corresponds to a class and each column corresponds to a binary learner. The class order (rows) corresponds to the order in `ClassNames`. Compose the matrix by following these guidelines:

• Every element of the custom coding matrix must be `-1`, `0`, or `1`, and the value must correspond to a dichotomous class assignment. This table describes the meaning of `Coding(i,j)`, that is, the class that learner `j` assigns to observations in class `i`.

ValueDichotomous Class Assignment
`–1`Learner `j` assigns observations in class `i` to a negative class.
`0`Before training, learner `j` removes observations in class `i` from the data set.
`1`Learner `j` assigns observations in class `i` to a positive class.

• Every column must contain at least one `-1` or `1`.

• For all column indices `i`,`j` such that `i``j`, `Coding(:,i)` cannot equal `Coding(:,j)` and `Coding(:,i)` cannot equal `-Coding(:,j)`.

• All rows of the custom coding matrix must be different.

For more details on the form of custom coding design matrices, see Custom Coding Design Matrices.

Example: `'Coding','ternarycomplete'`

Data Types: `char` | `string` | `double` | `single` | `int16` | `int32` | `int64` | `int8`

Flag indicating whether to transform scores to posterior probabilities, specified as the comma-separated pair consisting of `'FitPosterior'` and a `true` (`1`) or `false` (`0`).

If `FitPosterior` is `true`, then the software transforms binary-learner classification scores to posterior probabilities. You can obtain posterior probabilities by using `kfoldPredict`, `predict`, or `resubPredict`.

`fitcecoc` does not support fitting posterior probabilities if:

• The ensemble method is `AdaBoostM2`, `LPBoost`, `RUSBoost`, `RobustBoost`, or `TotalBoost`.

• The binary learners (`Learners`) are linear or kernel classification models that implement SVM. To obtain posterior probabilities for linear or kernel classification models, implement logistic regression instead.

Example: `'FitPosterior',true`

Data Types: `logical`

Binary learner templates, specified as the comma-separated pair consisting of `'Learners'` and a character vector, string scalar, template object, or cell vector of template objects. Specifically, you can specify binary classifiers such as SVM, and the ensembles that use `GentleBoost`, `LogitBoost`, and `RobustBoost`, to solve multiclass problems. However, `fitcecoc` also supports multiclass models as binary classifiers.

By default, the software trains learners using default SVM templates.

Example: `'Learners','tree'`

## Output Arguments

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ECOC classification template, returned as a template object. Pass `t` to `testckfold` to specify how to create an ECOC classifier whose predictive performance you want to compare with another classifier.

If you display `t` to the Command Window, then all, unspecified options appear empty (`[]`). However, the software replaces empty options with their corresponding default values during training.

Introduced in R2015a