# confusionmat

Compute confusion matrix for classification problem

## Syntax

``C = confusionmat(group,grouphat)``
``C = confusionmat(group,grouphat,'Order',grouporder)``
``[C,order] = confusionmat(___)``

## Description

example

``C = confusionmat(group,grouphat)` returns the confusion matrix `C` determined by the known and predicted groups in `group` and `grouphat`, respectively.`

example

``C = confusionmat(group,grouphat,'Order',grouporder)` uses `grouporder` to order the rows and columns of `C`.`

example

``[C,order] = confusionmat(___)` also returns the order of the rows and columns of `C` in the variable `order` using any of the input arguments in previous syntaxes.`

## Examples

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Display the confusion matrix for data with two misclassifications and one missing classification.

Create vectors for the known groups and the predicted groups.

```g1 = [3 2 2 3 1 1]'; % Known groups g2 = [4 2 3 NaN 1 1]'; % Predicted groups```

Return the confusion matrix.

`C = confusionmat(g1,g2)`
```C = 4×4 2 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 ```

The indices of the rows and columns of the confusion matrix `C` are identical and arranged by default in the sorted order of `[g1;g2]`, that is, `(1,2,3,4)`.

The confusion matrix shows that the two data points known to be in group 1 are classified correctly. For group 2, one of the data points is misclassified into group 3. Also, one of the data points known to be in group 3 is misclassified into group 4. `confusionmat` treats the `NaN` value in the grouping variable `g2` as a missing value and does not include it in the rows and columns of `C`.

Plot the confusion matrix as a confusion matrix chart by using `confusionchart`.

`confusionchart(C)`

You do not need to calculate the confusion matrix first and then plot it. Instead, plot a confusion matrix chart directly from the true and predicted labels by using `confusionchart`.

`cm = confusionchart(g1,g2)`

```cm = ConfusionMatrixChart with properties: NormalizedValues: [4x4 double] ClassLabels: [4x1 double] Use GET to show all properties ```

The `ConfusionMatrixChart` object stores the numeric confusion matrix in the `NormalizedValues` property and the classes in the `ClassLabels` property. Display these properties using dot notation.

`cm.NormalizedValues`
```ans = 4×4 2 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 ```
`cm.ClassLabels`
```ans = 4×1 1 2 3 4 ```

Display the confusion matrix for data with two misclassifications and one missing classification, and specify the group order.

Create vectors for the known groups and the predicted groups.

```g1 = [3 2 2 3 1 1]'; % Known groups g2 = [4 2 3 NaN 1 1]'; % Predicted groups```

Specify the group order and return the confusion matrix.

`C = confusionmat(g1,g2,'Order',[4 3 2 1])`
```C = 4×4 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 2 ```

The indices of the rows and columns of the confusion matrix `C` are identical and arranged in the order specified by the group order, that is, `(4,3,2,1)`.

The second row of the confusion matrix `C` shows that one of the data points known to be in group 3 is misclassified into group 4. The third row of `C` shows that one of the data points belonging to group 2 is misclassified into group 3, and the fourth row shows that the two data points known to be in group 1 are classified correctly. `confusionmat` treats the `NaN` value in the grouping variable `g2` as a missing value and does not include it in the rows and columns of `C`.

Perform classification on a sample of the `fisheriris` data set and display the confusion matrix for the resulting classification.

`load fisheriris`

Randomize the measurements and groups in the data.

```rng(0,'twister'); % For reproducibility numObs = length(species); p = randperm(numObs); meas = meas(p,:); species = species(p);```

Train a discriminant analysis classifier by using measurements in the first half of the data.

```half = floor(numObs/2); training = meas(1:half,:); trainingSpecies = species(1:half); Mdl = fitcdiscr(training,trainingSpecies);```

Predict labels for the measurements in the second half of the data by using the trained classifier.

```sample = meas(half+1:end,:); grouphat = predict(Mdl,sample);```

Specify the group order and display the confusion matrix for the resulting classification.

```group = species(half+1:end); [C,order] = confusionmat(group,grouphat,'Order',{'setosa','versicolor','virginica'})```
```C = 3×3 29 0 0 0 22 2 0 0 22 ```
```order = 3x1 cell {'setosa' } {'versicolor'} {'virginica' } ```

The confusion matrix shows that the measurements belonging to setosa and virginica are classified correctly, while two of the measurements belonging to versicolor are misclassified as virginica. The output `order` contains the order of the rows and columns of the confusion matrix in the sequence specified by the group order` {'setosa','versicolor','virginica'}`.

Perform classification on a tall array of the `fisheriris` data set, compute a confusion matrix for the known and predicted tall labels by using the `confusionmat` function, and plot the confusion matrix by using the `confusionchart` function.

When you perform calculations on tall arrays, MATLAB® uses either a parallel pool (default if you have Parallel Computing Toolbox™) or the local MATLAB session. If you want to run the example using the local MATLAB session when you have Parallel Computing Toolbox, you can change the global execution environment by using the `mapreducer` function.

`load fisheriris`

Convert the in-memory arrays `meas` and `species` to tall arrays.

`tx = tall(meas);`
```Starting parallel pool (parpool) using the 'local' profile ... Connected to the parallel pool (number of workers: 6). ```
`ty = tall(species);`

Find the number of observations in the tall array.

`numObs = gather(length(ty)); % gather collects tall array into memory`

Set the seeds of the random number generators using `rng` and `tallrng` for reproducibility, and randomly select training samples. The results can vary depending on the number of workers and the execution environment for the tall arrays. For details, see Control Where Your Code Runs.

```rng('default') tallrng('default') numTrain = floor(numObs/2); [txTrain,trIdx] = datasample(tx,numTrain,'Replace',false); tyTrain = ty(trIdx); ```

Fit a decision tree classifier model on the training samples.

`mdl = fitctree(txTrain,tyTrain); `
```Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 2: Completed in 3.9 sec - Pass 2 of 2: Completed in 1.5 sec Evaluation completed in 7.3 sec Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 4: Completed in 0.88 sec - Pass 2 of 4: Completed in 1.6 sec - Pass 3 of 4: Completed in 4 sec - Pass 4 of 4: Completed in 2.7 sec Evaluation completed in 11 sec Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 4: Completed in 0.54 sec - Pass 2 of 4: Completed in 1.2 sec - Pass 3 of 4: Completed in 3 sec - Pass 4 of 4: Completed in 2 sec Evaluation completed in 7.6 sec Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 4: Completed in 0.51 sec - Pass 2 of 4: Completed in 1.3 sec - Pass 3 of 4: Completed in 3.1 sec - Pass 4 of 4: Completed in 2.5 sec Evaluation completed in 8.5 sec Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 4: Completed in 0.42 sec - Pass 2 of 4: Completed in 1.2 sec - Pass 3 of 4: Completed in 3 sec - Pass 4 of 4: Completed in 2.1 sec Evaluation completed in 7.6 sec ```

Predict labels for the test samples by using the trained model.

```txTest = tx(~trIdx,:); label = predict(mdl,txTest);```

Compute the confusion matrix for the resulting classification.

```tyTest = ty(~trIdx); [C,order] = confusionmat(tyTest,label)```
```C = M×N×... tall array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : : Preview deferred. Learn more. order = M×N×... tall array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : : Preview deferred. Learn more. ```

Use the `gather` function to perform the deferred calculation and return the result of `confusionmat` in memory.

`gather(C)`
```Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 1: Completed in 1.9 sec Evaluation completed in 2.3 sec ```
```ans = 3×3 20 0 0 1 30 2 0 0 22 ```
`gather(order)`
```Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 0.032 sec ```
```ans = 3×1 cell {'setosa' } {'versicolor'} {'virginica' } ```

The confusion matrix shows that three measurements in the versicolor class are misclassified. All the measurements belonging to setosa and virginica are classified correctly.

To compute and plot the confusion matrix, use `confusionchart` instead.

`cm = confusionchart(tyTest,label)`
```Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 1: Completed in 0.34 sec Evaluation completed in 0.6 sec Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 1: Completed in 0.48 sec Evaluation completed in 0.67 sec ```

```cm = ConfusionMatrixChart with properties: NormalizedValues: [3×3 double] ClassLabels: {3×1 cell} Show all properties ```

Use the `confusionmat` function to create a matrix showing the number of flights that travel between airports listed in the columns of a tall table.

When you perform calculations on tall arrays, MATLAB® uses either a parallel pool (default if you have Parallel Computing Toolbox™) or the local MATLAB session. To run the example using the local MATLAB session when you have Parallel Computing Toolbox, change the global execution environment by using the `mapreducer` function.

`mapreducer(0)`

Create a datastore for the `airlinesmall.csv` data set. Treat `'NA'` values as missing data so that they are replaced with `NaN` values. Select the variables `Origin` and `Dest` to include in the datastore.

```varnames = {'Origin','Dest'}; ds = datastore('airlinesmall.csv','TreatAsMissing','NA', ... 'SelectedVariableNames',varnames);```

Create a tall array for the data in the datastore. Because the data in `ds` is tabular, the result is a tall table. If the data is not tabular, then `tall` creates a tall cell array instead.

`T = tall(ds)`
```T = Mx2 tall table Origin Dest _______ _______ {'LAX'} {'SJC'} {'SJC'} {'BUR'} {'SAN'} {'SMF'} {'BUR'} {'SJC'} {'SMF'} {'LAX'} {'LAX'} {'SJC'} {'SAN'} {'SFO'} {'SEA'} {'LAX'} : : : : ```

The display of the tall table indicates that the number of rows of data is unknown.

Create a matrix showing the number of flights between columns `T.Origin` and `T.Dest`. This matrix is not a confusion matrix, because the two columns do not contain known and predicted values from classification. However, you can use the `confusionmat` function to create a matrix of frequencies.

`[ta,tb] = confusionmat(T.Origin,T.Dest)`
```ta = MxNx... tall array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : : tb = MxNx... tall array ? ? ? ... ? ? ? ... ? ? ? ... : : : : : : ```

Perform the deferred calculation by using the `gather` function, and return the result of `confusionmat` in memory.

`[freqMatrix,airportOrder] = gather(ta,tb);`
```Evaluating tall expression using the Local MATLAB Session: - Pass 1 of 1: Completed in 1.3 sec Evaluation completed in 1.7 sec ```

Display the first five rows of the matrix `freqMatrix` and the corresponding order of rows and columns `airportOrder`.

`freqMatrix(1:5,:)`
```ans = 5×323 0 153 169 0 91 161 322 0 44 6 56 24 0 0 23 180 122 20 150 20 63 77 134 37 10 0 3 51 0 1 311 0 15 0 32 81 30 53 0 9 2 15 12 293 20 38 1 73 0 41 168 0 75 59 5 76 0 6 14 79 0 1 0 0 0 54 60 0 5 0 1 5 51 0 0 0 0 1 0 0 55 0 0 0 8 67 50 0 0 0 0 18 1 59 1 0 0 11 0 4 187 87 0 0 78 39 120 0 14 1 18 19 0 0 0 98 95 2 19 3 14 14 72 0 0 0 0 0 0 0 108 0 1 0 1 31 4 14 0 1 0 3 9 172 5 13 0 21 0 10 0 58 0 0 61 25 83 3 2 1 0 0 0 0 0 0 23 0 5 0 0 0 21 0 0 0 0 0 0 0 87 0 0 0 0 13 0 0 0 0 0 0 0 67 0 0 0 1 0 0 114 1 88 73 0 70 20 5 4 47 1 3 0 0 0 40 39 0 1 0 0 3 57 0 0 0 0 0 0 0 50 0 1 0 1 28 1 0 0 0 0 0 2 58 5 0 0 21 0 0 ```
`airportOrder(1:5)`
```ans = 5x1 cell {'LAX'} {'SJC'} {'SAN'} {'BUR'} {'SMF'} ```

The matrix `freqMatrix` displays the number of flights from an origin airport (row) to a destination airport (column). For example, a total of `168` flights leave `SJC` and arrive at `LAX` (see `freqMatrix(2,1)`). Similarly, `88` flights leave `SMF` and arrive at `SAN` (see `freqMatrix(5,3)`). As noted earlier, `freqMatrix` is not a confusion matrix, but shows a count of flights between airports. As expected, the diagonal elements are all zeros, because the origin and destination airport are always different.

## Input Arguments

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Known groups for categorizing observations, specified as a numeric vector, logical vector, character array, string array, cell array of character vectors, or categorical vector.

`group` is a grouping variable of the same type as `grouphat`. The `group` argument must have the same number of observations as `grouphat`, as described in Grouping Variables. The `confusionmat` function treats character arrays and string arrays as cell arrays of character vectors. Additionally, `confusionmat` treats `NaN`, empty, and `'undefined'` values in `group` as missing values and does not count them as distinct groups or categories.

Example: `{'Male','Female','Female','Male','Female'}`

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

Predicted groups for categorizing observations, specified as a numeric vector, logical vector, character array, string array, cell array of character vectors, or categorical vector.

`grouphat` is a grouping variable of the same type as `group`. The `grouphat` argument must have the same number of observations as `group`, as described in Grouping Variables. The `confusionmat` function treats character arrays and string arrays as cell arrays of character vectors. Additionally, `confusionmat` treats `NaN`, empty, and `'undefined'` values in `grouphat` as missing values and does not count them as distinct groups or categories.

Example: `[1 0 0 1 0]`

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

Group order, specified as a numeric vector, logical vector, character array, string array, cell array of character vectors, or categorical vector.

`grouporder` is a grouping variable containing all the distinct elements in `group` and `grouphat`. Specify `grouporder` to define the order of the rows and columns of `C`. If `grouporder` contains elements that are not in `group` or `grouphat`, the corresponding entries in `C` are `0`.

By default, the group order depends on the data type of `s = [group;grouphat]`:

• For numeric and logical vectors, the order is the sorted order of `s`.

• For categorical vectors, the order is the order returned by `categories(s)`.

• For other data types, the order is the order of first appearance in `s`.

Example: `'order',{'setosa','versicolor','virginica'}`

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

## Output Arguments

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Confusion matrix, returned as a square matrix with size equal to the total number of distinct elements in the `group` and `grouphat` arguments. `C(i,j)` is the count of observations known to be in group `i` but predicted to be in group `j`.

The rows and columns of `C` have identical ordering of the same group indices. By default, the group order depends on the data type of `s = [group;grouphat]`:

• For numeric and logical vectors, the order is the sorted order of `s`.

• For categorical vectors, the order is the order returned by `categories(s)`.

• For other data types, the order is the order of first appearance in `s`.

To change the order, specify `grouporder`,

The `confusionmat` function treats `NaN`, empty, and `'undefined'` values in the grouping variables as missing values and does not include them in the rows and columns of `C`.

Order of rows and columns in `C`, returned as a numeric vector, logical vector, categorical vector, or cell array of character vectors. If `group` and `grouphat` are character arrays, string arrays, or cell arrays of character vectors, then the variable `order` is a cell array of character vectors. Otherwise, `order` is of the same type as `group` and `grouphat`.

## Alternative Functionality

• Use `confusionchart` to calculate and plot a confusion matrix. Additionally, `confusionchart` displays summary statistics about your data and sorts the classes of the confusion matrix according to the class-wise precision (positive predictive value), class-wise recall (true positive rate), or total number of correctly classified observations.

## Version History

Introduced in R2008b