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Regularization is a process of choosing fewer weak learners for an ensemble in a way that does not diminish predictive performance. Currently you can regularize regression ensembles. (You can also regularize a discriminant analysis classifier in a non-ensemble context; see Regularize Discriminant Analysis Classifier.)

The `regularize`

method finds an optimal set of learner weights *α _{t}* that minimize

$\sum}_{n=1}^{N}{w}_{n}g\left(\left({\displaystyle \sum}_{t=1}^{T}{\alpha}_{t}{h}_{t}\left({x}_{n}\right)\right),{y}_{n}\right)+\lambda {\displaystyle \sum}_{t=1}^{T}\left|{\alpha}_{t}\right|.$

Here

*λ*≥ 0 is a parameter you provide, called the lasso parameter.*h*is a weak learner in the ensemble trained on_{t}*N*observations with predictors*x*, responses_{n}*y*, and weights_{n}*w*._{n}*g*(*f*,*y*) = (*f*–*y*)^{2}is the squared error.

The ensemble is regularized on the same (*x _{n}*,

$\sum}_{n=1}^{N}{w}_{n}g\left(\left({\displaystyle \sum}_{t=1}^{T}{\alpha}_{t}{h}_{t}\left({x}_{n}\right)\right),{y}_{n}\right)$

is the ensemble resubstitution error. The error is measured by mean squared error (MSE).

If you use *λ* = 0, `regularize`

finds the weak learner weights by minimizing the resubstitution MSE. Ensembles tend to overtrain. In other words, the resubstitution error is typically smaller than the true generalization error. By making the resubstitution error even smaller, you are likely to make the ensemble accuracy worse instead of improving it. On the other hand, positive values of *λ* push the magnitude of the *α _{t}* coefficients to 0. This often improves the generalization error. Of course, if you choose

A nice feature of lasso regularization is its ability to drive the optimized coefficients precisely to 0. If a learner's weight *α _{t}* is 0, this learner can be excluded from the regularized ensemble. In the end, you get an ensemble with improved accuracy and fewer learners.

This example uses data for predicting the insurance risk of a car based on its many attributes.

Load the `imports-85`

data into the MATLAB workspace.

`load imports-85;`

Look at a description of the data to find the categorical variables and predictor names.

Description

`Description = `*9x79 char array*
'1985 Auto Imports Database from the UCI repository '
'http://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names'
'Variables have been reordered to place variables with numeric values (referred '
'to as "continuous" on the UCI site) to the left and categorical values to the '
'right. Specifically, variables 1:16 are: symboling, normalized-losses, '
'wheel-base, length, width, height, curb-weight, engine-size, bore, stroke, '
'compression-ratio, horsepower, peak-rpm, city-mpg, highway-mpg, and price. '
'Variables 17:26 are: make, fuel-type, aspiration, num-of-doors, body-style, '
'drive-wheels, engine-location, engine-type, num-of-cylinders, and fuel-system. '

The objective of this process is to predict the "symboling," the first variable in the data, from the other predictors. "symboling" is an integer from `-3`

(good insurance risk) to `3`

(poor insurance risk). You could use a classification ensemble to predict this risk instead of a regression ensemble. When you have a choice between regression and classification, you should try regression first.

Prepare the data for ensemble fitting.

Y = X(:,1); X(:,1) = []; VarNames = {'normalized-losses' 'wheel-base' 'length' 'width' 'height' ... 'curb-weight' 'engine-size' 'bore' 'stroke' 'compression-ratio' ... 'horsepower' 'peak-rpm' 'city-mpg' 'highway-mpg' 'price' 'make' ... 'fuel-type' 'aspiration' 'num-of-doors' 'body-style' 'drive-wheels' ... 'engine-location' 'engine-type' 'num-of-cylinders' 'fuel-system'}; catidx = 16:25; % indices of categorical predictors

Create a regression ensemble from the data using 300 trees.

ls = fitrensemble(X,Y,'Method','LSBoost','NumLearningCycles',300, ... 'LearnRate',0.1,'PredictorNames',VarNames, ... 'ResponseName','Symboling','CategoricalPredictors',catidx)

ls = RegressionEnsemble PredictorNames: {1x25 cell} ResponseName: 'Symboling' CategoricalPredictors: [16 17 18 19 20 21 22 23 24 25] ResponseTransform: 'none' NumObservations: 205 NumTrained: 300 Method: 'LSBoost' LearnerNames: {'Tree'} ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.' FitInfo: [300x1 double] FitInfoDescription: {2x1 cell} Regularization: [] Properties, Methods

The final line, `Regularization`

, is empty ([]). To regularize the ensemble, you have to use the `regularize`

method.

cv = crossval(ls,'KFold',5); figure; plot(kfoldLoss(cv,'Mode','Cumulative')); xlabel('Number of trees'); ylabel('Cross-validated MSE'); ylim([0.2,2])

It appears you might obtain satisfactory performance from a smaller ensemble, perhaps one containing from 50 to 100 trees.

Call the `regularize`

method to try to find trees that you can remove from the ensemble. By default, `regularize`

examines 10 values of the lasso (`Lambda`

) parameter spaced exponentially.

ls = regularize(ls)

ls = RegressionEnsemble PredictorNames: {1x25 cell} ResponseName: 'Symboling' CategoricalPredictors: [16 17 18 19 20 21 22 23 24 25] ResponseTransform: 'none' NumObservations: 205 NumTrained: 300 Method: 'LSBoost' LearnerNames: {'Tree'} ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.' FitInfo: [300x1 double] FitInfoDescription: {2x1 cell} Regularization: [1x1 struct] Properties, Methods

The `Regularization`

property is no longer empty.

Plot the resubstitution mean-squared error (MSE) and number of learners with nonzero weights against the lasso parameter. Separately plot the value at `Lambda = 0`

. Use a logarithmic scale because the values of `Lambda`

are exponentially spaced.

figure; semilogx(ls.Regularization.Lambda,ls.Regularization.ResubstitutionMSE, ... 'bx-','Markersize',10); line([1e-3 1e-3],[ls.Regularization.ResubstitutionMSE(1) ... ls.Regularization.ResubstitutionMSE(1)],... 'Marker','x','Markersize',10,'Color','b'); r0 = resubLoss(ls); line([ls.Regularization.Lambda(2) ls.Regularization.Lambda(end)],... [r0 r0],'Color','r','LineStyle','--'); xlabel('Lambda'); ylabel('Resubstitution MSE'); annotation('textbox',[0.5 0.22 0.5 0.05],'String','unregularized ensemble', ... 'Color','r','FontSize',14,'LineStyle','none');

figure; loglog(ls.Regularization.Lambda,sum(ls.Regularization.TrainedWeights>0,1)); line([1e-3 1e-3],... [sum(ls.Regularization.TrainedWeights(:,1)>0) ... sum(ls.Regularization.TrainedWeights(:,1)>0)],... 'marker','x','markersize',10,'color','b'); line([ls.Regularization.Lambda(2) ls.Regularization.Lambda(end)],... [ls.NTrained ls.NTrained],... 'color','r','LineStyle','--'); xlabel('Lambda'); ylabel('Number of learners'); annotation('textbox',[0.3 0.8 0.5 0.05],'String','unregularized ensemble',... 'color','r','FontSize',14,'LineStyle','none');

The resubstitution MSE values are likely to be overly optimistic. To obtain more reliable estimates of the error associated with various values of `Lambda`

, cross validate the ensemble using `cvshrink`

. Plot the resulting cross-validation loss (MSE) and number of learners against `Lambda`

.

rng(0,'Twister') % for reproducibility [mse,nlearn] = cvshrink(ls,'Lambda',ls.Regularization.Lambda,'KFold',5);

Warning: Some folds do not have any trained weak learners.

figure; semilogx(ls.Regularization.Lambda,ls.Regularization.ResubstitutionMSE, ... 'bx-','Markersize',10); hold on; semilogx(ls.Regularization.Lambda,mse,'ro-','Markersize',10); hold off; xlabel('Lambda'); ylabel('Mean squared error'); legend('resubstitution','cross-validation','Location','NW'); line([1e-3 1e-3],[ls.Regularization.ResubstitutionMSE(1) ... ls.Regularization.ResubstitutionMSE(1)],... 'Marker','x','Markersize',10,'Color','b','HandleVisibility','off'); line([1e-3 1e-3],[mse(1) mse(1)],'Marker','o',... 'Markersize',10,'Color','r','LineStyle','--','HandleVisibility','off');

figure; loglog(ls.Regularization.Lambda,sum(ls.Regularization.TrainedWeights>0,1)); hold;

Current plot held

loglog(ls.Regularization.Lambda,nlearn,'r--'); hold off; xlabel('Lambda'); ylabel('Number of learners'); legend('resubstitution','cross-validation','Location','NE'); line([1e-3 1e-3],... [sum(ls.Regularization.TrainedWeights(:,1)>0) ... sum(ls.Regularization.TrainedWeights(:,1)>0)],... 'Marker','x','Markersize',10,'Color','b','HandleVisibility','off'); line([1e-3 1e-3],[nlearn(1) nlearn(1)],'marker','o',... 'Markersize',10,'Color','r','LineStyle','--','HandleVisibility','off');

Examining the cross-validated error shows that the cross-validation MSE is almost flat for `Lambda`

up to a bit over `1e-2`

.

Examine `ls.Regularization.Lambda`

to find the highest value that gives MSE in the flat region (up to a bit over `1e-2`

).

jj = 1:length(ls.Regularization.Lambda); [jj;ls.Regularization.Lambda]

`ans = `*2×10*
1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 10.0000
0 0.0019 0.0045 0.0107 0.0254 0.0602 0.1428 0.3387 0.8033 1.9048

Element `5`

of `ls.Regularization.Lambda`

has value `0.0254`

, the largest in the flat range.

Reduce the ensemble size using the `shrink`

method. `shrink`

returns a compact ensemble with no training data. The generalization error for the new compact ensemble was already estimated by cross validation in `mse(5)`

.

`cmp = shrink(ls,'weightcolumn',5)`

cmp = CompactRegressionEnsemble PredictorNames: {1x25 cell} ResponseName: 'Symboling' CategoricalPredictors: [16 17 18 19 20 21 22 23 24 25] ResponseTransform: 'none' NumTrained: 8 Properties, Methods

The number of trees in the new ensemble has notably reduced from the 300 in `ls`

.

Compare the sizes of the ensembles.

sz(1) = whos('cmp'); sz(2) = whos('ls'); [sz(1).bytes sz(2).bytes]

`ans = `*1×2*
91209 3227100

The size of the reduced ensemble is a fraction of the size of the original. Note that your ensemble sizes can vary depending on your operating system.

Compare the MSE of the reduced ensemble to that of the original ensemble.

figure; plot(kfoldLoss(cv,'mode','cumulative')); hold on plot(cmp.NTrained,mse(5),'ro','MarkerSize',10); xlabel('Number of trees'); ylabel('Cross-validated MSE'); legend('unregularized ensemble','regularized ensemble',... 'Location','NE'); hold off

The reduced ensemble gives low loss while using many fewer trees.

`fitrensemble`

| `regularize`

| `kfoldLoss`

| `cvshrink`

| `shrink`

| `resubLoss`

| `crossval`