# loss

Evaluate accuracy of learned feature weights on test data

## Syntax

``err = loss(mdl,Tbl,ResponseVarName)``
``err = loss(mdl,Tbl,Y)``
``err = loss(mdl,X,Y)``
``err = loss(___,LossFunction=lossFunc)``

## Description

````err = loss(mdl,Tbl,ResponseVarName)` computes the misclassification error of the model `mdl`, for the predictors in table `Tbl`, and the variable `ResponseVarName` in table `Tbl`. Use this syntax if `mdl` was originally trained on a table.```
````err = loss(mdl,Tbl,Y)` computes the misclassification error of the model `mdl`, for the predictors in table `Tbl` and the class labels in `Y`. Use this syntax if `mdl` was originally trained on a table.```
````err = loss(mdl,X,Y)` computes the misclassification error of the model `mdl`, for the predictors in matrix `X` and the class labels in `Y`. Use this syntax if `mdl` was originally trained on a numeric matrix.```

example

````err = loss(___,LossFunction=lossFunc)` computes the classification error according to the specified loss function type. Use the loss function specification in addition to the input arguments in previous syntaxes.```

## Examples

collapse all

`load("twodimclassdata.mat")`

This data set is simulated using the scheme described in [1]. This is a two-class classification problem in two dimensions. Data from the first class (class –1) are drawn from two bivariate normal distributions $N\left({\mu }_{1},\Sigma \right)$ or $N\left({\mu }_{2},\Sigma \right)$ with equal probability, where ${\mu }_{1}=\left[-0.75,-1.5\right]$, ${\mu }_{2}=\left[0.75,1.5\right]$, and $\Sigma ={I}_{2}$. Similarly, data from the second class (class 1) are drawn from two bivariate normal distributions $N\left({\mu }_{3},\Sigma \right)$ or $N\left({\mu }_{4},\Sigma \right)$ with equal probability, where ${\mu }_{3}=\left[1.5,-1.5\right]$, ${\mu }_{4}=\left[-1.5,1.5\right]$, and $\Sigma ={I}_{2}$. The normal distribution parameters used to create this data set result in tighter clusters in data than the data used in [1].

Create a scatter plot of the data grouped by the class.

```gscatter(X(:,1),X(:,2),y) xlabel("x1") ylabel("x2")```

Add 100 irrelevant features to $X$. First generate data from a Normal distribution with a mean of 0 and a variance of 20.

```n = size(X,1); rng("default") XwithBadFeatures = [X,randn(n,100)*sqrt(20)];```

Normalize the data so that all points are between 0 and 1.

```XwithBadFeatures = (XwithBadFeatures-min(XwithBadFeatures,[],1))./ ... range(XwithBadFeatures,1); X = XwithBadFeatures;```

Fit a neighborhood component analysis (NCA) model to the data using the default `Lambda` (regularization parameter, $\lambda$) value. Use the LBFGS solver and display the convergence information.

```ncaMdl = fscnca(X,y,FitMethod="exact",Verbose=1, ... Solver="lbfgs");```
``` o Solver = LBFGS, HessianHistorySize = 15, LineSearchMethod = weakwolfe |====================================================================================================| | ITER | FUN VALUE | NORM GRAD | NORM STEP | CURV | GAMMA | ALPHA | ACCEPT | |====================================================================================================| | 0 | 9.519258e-03 | 1.494e-02 | 0.000e+00 | | 4.015e+01 | 0.000e+00 | YES | | 1 | -3.093574e-01 | 7.186e-03 | 4.018e+00 | OK | 8.956e+01 | 1.000e+00 | YES | | 2 | -4.809455e-01 | 4.444e-03 | 7.123e+00 | OK | 9.943e+01 | 1.000e+00 | YES | | 3 | -4.938877e-01 | 3.544e-03 | 1.464e+00 | OK | 9.366e+01 | 1.000e+00 | YES | | 4 | -4.964759e-01 | 2.901e-03 | 6.084e-01 | OK | 1.554e+02 | 1.000e+00 | YES | | 5 | -4.972077e-01 | 1.323e-03 | 6.129e-01 | OK | 1.195e+02 | 5.000e-01 | YES | | 6 | -4.974743e-01 | 1.569e-04 | 2.155e-01 | OK | 1.003e+02 | 1.000e+00 | YES | | 7 | -4.974868e-01 | 3.844e-05 | 4.161e-02 | OK | 9.835e+01 | 1.000e+00 | YES | | 8 | -4.974874e-01 | 1.417e-05 | 1.073e-02 | OK | 1.043e+02 | 1.000e+00 | YES | | 9 | -4.974874e-01 | 4.893e-06 | 1.781e-03 | OK | 1.530e+02 | 1.000e+00 | YES | | 10 | -4.974874e-01 | 9.404e-08 | 8.947e-04 | OK | 1.670e+02 | 1.000e+00 | YES | Infinity norm of the final gradient = 9.404e-08 Two norm of the final step = 8.947e-04, TolX = 1.000e-06 Relative infinity norm of the final gradient = 9.404e-08, TolFun = 1.000e-06 EXIT: Local minimum found. ```

Plot the feature weights. The weights of the irrelevant features should be very close to zero.

```semilogx(ncaMdl.FeatureWeights,"o") xlabel("Feature index") ylabel("Feature weight") grid on```

Predict the classes using the NCA model and compute the confusion matrix.

```ypred = predict(ncaMdl,X); confusionchart(y,ypred)```

The confusion matrix shows that 40 of the data that are in class –1 are predicted as belonging to class –1, and 60 of the data from class –1 are predicted to be in class 1. Similarly, 94 of the data from class 1 are predicted to be from class 1, and 6 of them are predicted to be from class –1. The prediction accuracy for class –1 is not good.

All weights are very close to zero, which indicates that the value of $\lambda$ used in training the model is too large. When $\lambda \to \infty$, all features weights approach to zero. Hence, it is important to tune the regularization parameter in most cases to detect the relevant features.

Use five-fold cross-validation to tune $\lambda$ for feature selection by using `fscnca`. Tuning $\lambda$ means finding the $\lambda$ value that will produce the minimum classification loss. To tune $\lambda$ using cross-validation:

1. Partition the data into five folds. For each fold, `cvpartition` assigns four-fifths of the data as a training set and one-fifth of the data as a test set. Again for each fold, `cvpartition` creates a stratified partition, where each partition has roughly the same proportion of classes.

```cvp = cvpartition(y,"KFold",5); numtestsets = cvp.NumTestSets; lambdavalues = linspace(0,2,20)/length(y); lossvalues = zeros(length(lambdavalues),numtestsets);```

2. Train the neighborhood component analysis (NCA) model for each $\lambda$ value using the training set in each fold.

3. Compute the classification loss for the corresponding test set in the fold using the NCA model. Record the loss value.

4. Repeat this process for all folds and all $\lambda$ values.

```for i = 1:length(lambdavalues) for k = 1:numtestsets % Extract the training set from the partition object Xtrain = X(cvp.training(k),:); ytrain = y(cvp.training(k),:); % Extract the test set from the partition object Xtest = X(cvp.test(k),:); ytest = y(cvp.test(k),:); % Train an NCA model for classification using the training set ncaMdl = fscnca(Xtrain,ytrain,FitMethod="exact", ... Solver="lbfgs",Lambda=lambdavalues(i)); % Compute the classification loss for the test set using the NCA % model lossvalues(i,k) = loss(ncaMdl,Xtest,ytest, ... LossFunction="quadratic"); end end```

Plot the average loss values of the folds versus the $\lambda$ values. If the $\lambda$ value that corresponds to the minimum loss falls on the boundary of the tested $\lambda$ values, the range of $\lambda$ values should be reconsidered.

```plot(lambdavalues,mean(lossvalues,2),"o-") xlabel("Lambda values") ylabel("Loss values") grid on```

Find the $\lambda$ value that corresponds to the minimum average loss.

```[~,idx] = min(mean(lossvalues,2)); % Find the index bestlambda = lambdavalues(idx) % Find the best lambda value```
```bestlambda = 0.0037 ```

Fit the NCA model to all of the data using the best $\lambda$ value. Use the LBFGS solver and display the convergence information.

```ncaMdl = fscnca(X,y,FitMethod="exact",Verbose=1, ... Solver="lbfgs",Lambda=bestlambda);```
``` o Solver = LBFGS, HessianHistorySize = 15, LineSearchMethod = weakwolfe |====================================================================================================| | ITER | FUN VALUE | NORM GRAD | NORM STEP | CURV | GAMMA | ALPHA | ACCEPT | |====================================================================================================| | 0 | -1.246913e-01 | 1.231e-02 | 0.000e+00 | | 4.873e+01 | 0.000e+00 | YES | | 1 | -3.411330e-01 | 5.717e-03 | 3.618e+00 | OK | 1.068e+02 | 1.000e+00 | YES | | 2 | -5.226111e-01 | 3.763e-02 | 8.252e+00 | OK | 7.825e+01 | 1.000e+00 | YES | | 3 | -5.817731e-01 | 8.496e-03 | 2.340e+00 | OK | 5.591e+01 | 5.000e-01 | YES | | 4 | -6.132632e-01 | 6.863e-03 | 2.526e+00 | OK | 8.228e+01 | 1.000e+00 | YES | | 5 | -6.135264e-01 | 9.373e-03 | 7.341e-01 | OK | 3.244e+01 | 1.000e+00 | YES | | 6 | -6.147894e-01 | 1.182e-03 | 2.933e-01 | OK | 2.447e+01 | 1.000e+00 | YES | | 7 | -6.148714e-01 | 6.392e-04 | 6.688e-02 | OK | 3.195e+01 | 1.000e+00 | YES | | 8 | -6.149524e-01 | 6.521e-04 | 9.934e-02 | OK | 1.236e+02 | 1.000e+00 | YES | | 9 | -6.149972e-01 | 1.154e-04 | 1.191e-01 | OK | 1.171e+02 | 1.000e+00 | YES | | 10 | -6.149990e-01 | 2.922e-05 | 1.983e-02 | OK | 7.365e+01 | 1.000e+00 | YES | | 11 | -6.149993e-01 | 1.556e-05 | 8.354e-03 | OK | 1.288e+02 | 1.000e+00 | YES | | 12 | -6.149994e-01 | 1.147e-05 | 7.256e-03 | OK | 2.332e+02 | 1.000e+00 | YES | | 13 | -6.149995e-01 | 1.040e-05 | 6.781e-03 | OK | 2.287e+02 | 1.000e+00 | YES | | 14 | -6.149996e-01 | 9.015e-06 | 6.265e-03 | OK | 9.974e+01 | 1.000e+00 | YES | | 15 | -6.149996e-01 | 7.763e-06 | 5.206e-03 | OK | 2.919e+02 | 1.000e+00 | YES | | 16 | -6.149997e-01 | 8.374e-06 | 1.679e-02 | OK | 6.878e+02 | 1.000e+00 | YES | | 17 | -6.149997e-01 | 9.387e-06 | 9.542e-03 | OK | 1.284e+02 | 5.000e-01 | YES | | 18 | -6.149997e-01 | 3.250e-06 | 5.114e-03 | OK | 1.225e+02 | 1.000e+00 | YES | | 19 | -6.149997e-01 | 1.574e-06 | 1.275e-03 | OK | 1.808e+02 | 1.000e+00 | YES | |====================================================================================================| | ITER | FUN VALUE | NORM GRAD | NORM STEP | CURV | GAMMA | ALPHA | ACCEPT | |====================================================================================================| | 20 | -6.149997e-01 | 5.764e-07 | 6.765e-04 | OK | 2.905e+02 | 1.000e+00 | YES | Infinity norm of the final gradient = 5.764e-07 Two norm of the final step = 6.765e-04, TolX = 1.000e-06 Relative infinity norm of the final gradient = 5.764e-07, TolFun = 1.000e-06 EXIT: Local minimum found. ```

Plot the feature weights.

```semilogx(ncaMdl.FeatureWeights,"o") xlabel("Feature index") ylabel("Feature weight") grid on```

`fscnca` correctly figures out that the first two features are relevant and that the rest are not. The first two features are not individually informative, but when taken together result in an accurate classification model.

Predict the classes using the new model and compute the accuracy.

```ypred = predict(ncaMdl,X); confusionchart(y,ypred)```

Confusion matrix shows that prediction accuracy for class –1 has improved. 88 of the data from class –1 are predicted to be from –1, and 12 of them are predicted to be from class 1. Additionally, 92 of the data from class 1 are predicted to be from class 1, and 8 of them are predicted to be from class –1.

References

[1] Yang, W., K. Wang, W. Zuo. "Neighborhood Component Feature Selection for High-Dimensional Data." Journal of Computers. Vol. 7, Number 1, January, 2012.

## Input Arguments

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Neighborhood component analysis model for classification, specified as a `FeatureSelectionNCAClassification` object.

Sample data, specified as a table. Each row of `Tbl` corresponds to one observation, and each column corresponds to one predictor variable.

Data Types: `table`

Response variable name, specified as the name of a variable in `Tbl`. The remaining variables in the table are predictors.

Data Types: `char` | `string`

Predictor variable values, specified as an n-by-p matrix, where n is the number of observations and p is the number of predictor variables.

Data Types: `single` | `double`

Class labels, specified as a categorical array, logical vector, numeric vector, string array, cell array of character vectors of length n, or character matrix with n rows. n is the number of observations. Element i or row i of `Y` is the class label corresponding to row i of `X` (observation i).

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

Loss function type, specified as one of these values:

• `"classiferror"` — Misclassification rate in decimal, defined as

`$\frac{1}{n}\sum _{i=1}^{n}I\left({k}_{i}\ne {t}_{i}\right),$`

where ${k}_{i}$ is the predicted class and ${t}_{i}$ is the true class for observation i. $I\left({k}_{i}\ne {t}_{i}\right)$ is the indicator for when the ${k}_{i}$ is not the same as ${t}_{i}$.

• `"quadratic"` — Quadratic loss function, defined as

`$\frac{1}{n}\sum _{i=1}^{n}\sum _{k=1}^{c}{\left({p}_{ik}-I\left(i,k\right)\right)}^{2},$`

where c is the number of classes, ${p}_{ik}$ is the estimate probability that ith observation belongs to class k, and $I\left(i,k\right)$ is the indicator that ith observation belongs to class k.

Example: `"quadratic"`

## Output Arguments

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Smaller-the-better accuracy measure for learned feature weights, returned as a scalar value. You can specify the measure of accuracy using the `LossFunction` name-value argument.

## Version History

Introduced in R2016b