# loss

Loss of k-nearest neighbor classifier

## Syntax

``L = loss(mdl,Tbl,ResponseVarName)``
``L = loss(mdl,Tbl,Y)``
``L = loss(mdl,X,Y)``
``L = loss(___,Name,Value)``

## Description

````L = loss(mdl,Tbl,ResponseVarName)` returns a scalar representing how well `mdl` classifies the data in `Tbl` when `Tbl.ResponseVarName` contains the true classifications. If `Tbl` contains the response variable used to train `mdl`, then you do not need to specify `ResponseVarName`.When computing the loss, the `loss` function normalizes the class probabilities in `Tbl.ResponseVarName` to the class probabilities used for training, which are stored in the `Prior` property of `mdl`.The meaning of the classification loss (`L`) depends on the loss function and weighting scheme, but, in general, better classifiers yield smaller classification loss values. For more details, see Classification Loss.```
````L = loss(mdl,Tbl,Y)` returns a scalar representing how well `mdl` classifies the data in `Tbl` when `Y` contains the true classifications.When computing the loss, the `loss` function normalizes the class probabilities in `Y` to the class probabilities used for training, which are stored in the `Prior` property of `mdl`.```

example

````L = loss(mdl,X,Y)` returns a scalar representing how well `mdl` classifies the data in `X` when `Y` contains the true classifications.When computing the loss, the `loss` function normalizes the class probabilities in `Y` to the class probabilities used for training, which are stored in the `Prior` property of `mdl`.```
````L = loss(___,Name,Value)` specifies options using one or more name-value pair arguments in addition to the input arguments in previous syntaxes. For example, you can specify the loss function and the classification weights. NoteIf the predictor data in `X` or `Tbl` contains any missing values and `LossFun` is not set to `"classifcost"`, `"classiferror"`, or `"mincost"`, the `loss` function can return NaN. For more details, see loss can return NaN for predictor data with missing values. ```

## Examples

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Create a k-nearest neighbor classifier for the Fisher iris data, where k = 5.

Load the Fisher iris data set.

`load fisheriris`

Create a classifier for five nearest neighbors.

`mdl = fitcknn(meas,species,'NumNeighbors',5);`

Examine the loss of the classifier for a mean observation classified as `'versicolor'`.

```X = mean(meas); Y = {'versicolor'}; L = loss(mdl,X,Y)```
```L = 0 ```

All five nearest neighbors classify as `'versicolor'`.

## Input Arguments

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k-nearest neighbor classifier model, specified as a `ClassificationKNN` object.

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. Multicolumn variables and cell arrays other than cell arrays of character vectors are not allowed.

If `Tbl` contains the response variable used to train `mdl`, then you do not need to specify `ResponseVarName` or `Y`.

If you train `mdl` using sample data contained in a table, then the input data for `loss` must also be in a table.

Data Types: `table`

Response variable name, specified as the name of a variable in `Tbl`. If `Tbl` contains the response variable used to train `mdl`, then you do not need to specify `ResponseVarName`.

You must specify `ResponseVarName` as a character vector or string scalar. For example, if the response variable is stored as `Tbl.response`, then specify it as `'response'`. Otherwise, the software treats all columns of `Tbl`, including `Tbl.response`, as predictors.

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

Data Types: `char` | `string`

Predictor data, specified as a numeric matrix. Each row of `X` represents one observation, and each column represents one variable.

Data Types: `single` | `double`

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

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

### Name-Value Arguments

Specify optional pairs of arguments as `Name1=Value1,...,NameN=ValueN`, where `Name` is the argument name and `Value` is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose `Name` in quotes.

Example: `loss(mdl,Tbl,'response','LossFun','exponential','Weights','w')` returns the weighted exponential loss of `mdl` classifying the data in `Tbl`. Here, `Tbl.response` is the response variable, and `Tbl.w` is the weight variable.

Loss function, specified as the comma-separated pair consisting of `'LossFun'` and a built-in loss function name or a function handle.

• The following table lists the available loss functions.

ValueDescription
`'binodeviance'`Binomial deviance
`'classifcost'`Observed misclassification cost
`'classiferror'`Misclassified rate in decimal
`'exponential'`Exponential loss
`'hinge'`Hinge loss
`'logit'`Logistic loss
`'mincost'`Minimal expected misclassification cost (for classification scores that are posterior probabilities)
`'quadratic'`Quadratic loss

`'mincost'` is appropriate for classification scores that are posterior probabilities. By default, k-nearest neighbor models return posterior probabilities as classification scores (see `predict`).

• You can specify a function handle for a custom loss function using `@` (for example, `@lossfun`). Let n be the number of observations in `X` and K be the number of distinct classes (`numel(mdl.ClassNames)`). Your custom loss function must have this form:

``function lossvalue = lossfun(C,S,W,Cost)``

• `C` is an n-by-K logical matrix with rows indicating the class to which the corresponding observation belongs. The column order corresponds to the class order in `mdl.ClassNames`. Construct `C` by setting ```C(p,q) = 1```, if observation `p` is in class `q`, for each row. Set all other elements of row `p` to `0`.

• `S` is an n-by-K numeric matrix of classification scores. The column order corresponds to the class order in `mdl.ClassNames`. The argument `S` is a matrix of classification scores, similar to the output of `predict`.

• `W` is an n-by-1 numeric vector of observation weights. If you pass `W`, the software normalizes the weights to sum to `1`.

• `Cost` is a K-by-K numeric matrix of misclassification costs. For example, ```Cost = ones(K) – eye(K)``` specifies a cost of `0` for correct classification and `1` for misclassification.

• The output argument `lossvalue` is a scalar.

For more details on loss functions, see Classification Loss.

Data Types: `char` | `string` | `function_handle`

Observation weights, specified as the comma-separated pair consisting of `'Weights'` and a numeric vector or the name of a variable in `Tbl`.

If you specify `Weights` as a numeric vector, then the size of `Weights` must be equal to the number of rows in `X` or `Tbl`.

If you specify `Weights` as the name of a variable in `Tbl`, the name must be a character vector or string scalar. For example, if the weights are stored as `Tbl.w`, then specify `Weights` as `'w'`. Otherwise, the software treats all columns of `Tbl`, including `Tbl.w`, as predictors.

`loss` normalizes the weights so that observation weights in each class sum to the prior probability of that class. When you supply `Weights`, `loss` computes the weighted classification loss.

Example: `'Weights','w'`

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

## Algorithms

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### Classification Loss

Classification loss functions measure the predictive inaccuracy of classification models. When you compare the same type of loss among many models, a lower loss indicates a better predictive model.

Consider the following scenario.

• L is the weighted average classification loss.

• n is the sample size.

• For binary classification:

• yj is the observed class label. The software codes it as –1 or 1, indicating the negative or positive class (or the first or second class in the `ClassNames` property), respectively.

• f(Xj) is the positive-class classification score for observation (row) j of the predictor data X.

• mj = yjf(Xj) is the classification score for classifying observation j into the class corresponding to yj. Positive values of mj indicate correct classification and do not contribute much to the average loss. Negative values of mj indicate incorrect classification and contribute significantly to the average loss.

• For algorithms that support multiclass classification (that is, K ≥ 3):

• yj* is a vector of K – 1 zeros, with 1 in the position corresponding to the true, observed class yj. For example, if the true class of the second observation is the third class and K = 4, then y2* = [`0 0 1 0`]′. The order of the classes corresponds to the order in the `ClassNames` property of the input model.

• f(Xj) is the length K vector of class scores for observation j of the predictor data X. The order of the scores corresponds to the order of the classes in the `ClassNames` property of the input model.

• mj = yj*f(Xj). Therefore, mj is the scalar classification score that the model predicts for the true, observed class.

• The weight for observation j is wj. The software normalizes the observation weights so that they sum to the corresponding prior class probability stored in the `Prior` property. Therefore,

`$\sum _{j=1}^{n}{w}_{j}=1.$`

Given this scenario, the following table describes the supported loss functions that you can specify by using the `LossFun` name-value argument.

Loss FunctionValue of `LossFun`Equation
Binomial deviance`'binodeviance'`$L=\sum _{j=1}^{n}{w}_{j}\mathrm{log}\left\{1+\mathrm{exp}\left[-2{m}_{j}\right]\right\}.$
Observed misclassification cost`'classifcost'`

$L=\sum _{j=1}^{n}{w}_{j}{c}_{{y}_{j}{\stackrel{^}{y}}_{j}},$

where ${\stackrel{^}{y}}_{j}$ is the class label corresponding to the class with the maximal score, and ${c}_{{y}_{j}{\stackrel{^}{y}}_{j}}$ is the user-specified cost of classifying an observation into class ${\stackrel{^}{y}}_{j}$ when its true class is yj.

Misclassified rate in decimal`'classiferror'`

$L=\sum _{j=1}^{n}{w}_{j}I\left\{{\stackrel{^}{y}}_{j}\ne {y}_{j}\right\},$

where I{·} is the indicator function.

Cross-entropy loss`'crossentropy'`

`'crossentropy'` is appropriate only for neural network models.

The weighted cross-entropy loss is

`$L=-\sum _{j=1}^{n}\frac{{\stackrel{˜}{w}}_{j}\mathrm{log}\left({m}_{j}\right)}{Kn},$`

where the weights ${\stackrel{˜}{w}}_{j}$ are normalized to sum to n instead of 1.

Exponential loss`'exponential'`$L=\sum _{j=1}^{n}{w}_{j}\mathrm{exp}\left(-{m}_{j}\right).$
Hinge loss`'hinge'`$L=\sum _{j=1}^{n}{w}_{j}\mathrm{max}\left\{0,1-{m}_{j}\right\}.$
Logit loss`'logit'`$L=\sum _{j=1}^{n}{w}_{j}\mathrm{log}\left(1+\mathrm{exp}\left(-{m}_{j}\right)\right).$
Minimal expected misclassification cost`'mincost'`

`'mincost'` is appropriate only if classification scores are posterior probabilities.

The software computes the weighted minimal expected classification cost using this procedure for observations j = 1,...,n.

1. Estimate the expected misclassification cost of classifying the observation Xj into the class k:

`${\gamma }_{jk}={\left(f{\left({X}_{j}\right)}^{\prime }C\right)}_{k}.$`

f(Xj) is the column vector of class posterior probabilities for the observation Xj. C is the cost matrix stored in the `Cost` property of the model.

2. For observation j, predict the class label corresponding to the minimal expected misclassification cost:

`${\stackrel{^}{y}}_{j}=\underset{k=1,...,K}{\text{argmin}}{\gamma }_{jk}.$`

3. Using C, identify the cost incurred (cj) for making the prediction.

The weighted average of the minimal expected misclassification cost loss is

`$L=\sum _{j=1}^{n}{w}_{j}{c}_{j}.$`

Quadratic loss`'quadratic'`$L=\sum _{j=1}^{n}{w}_{j}{\left(1-{m}_{j}\right)}^{2}.$

If you use the default cost matrix (whose element value is 0 for correct classification and 1 for incorrect classification), then the loss values for `'classifcost'`, `'classiferror'`, and `'mincost'` are identical. For a model with a nondefault cost matrix, the `'classifcost'` loss is equivalent to the `'mincost'` loss most of the time. These losses can be different if prediction into the class with maximal posterior probability is different from prediction into the class with minimal expected cost. Note that `'mincost'` is appropriate only if classification scores are posterior probabilities.

This figure compares the loss functions (except `'classifcost'`, `'crossentropy'`, and `'mincost'`) over the score m for one observation. Some functions are normalized to pass through the point (0,1).

### True Misclassification Cost

Two costs are associated with KNN classification: the true misclassification cost per class and the expected misclassification cost per observation.

You can set the true misclassification cost per class by using the `'Cost'` name-value pair argument when you run `fitcknn`. The value `Cost(i,j)` is the cost of classifying an observation into class `j` if 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

Two costs are associated with KNN classification: the true misclassification cost per class and the expected misclassification cost per observation. The third output of `predict` is the expected misclassification cost per observation.

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

`[label,score,cost] = predict(mdl,Xnew)`

returns a matrix `cost` of size `Nobs`-by-`K`, among other outputs. Each row of the `cost` matrix contains the expected (average) cost of classifying the observation into each of the `K` classes. `cost(n,j)` is

`$\sum _{i=1}^{K}\stackrel{^}{P}\left(i|Xnew\left(n\right)\right)C\left(j|i\right),$`

where

• K is the number of classes.

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

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

## Version History

Introduced in R2012a

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