## Initialize Incremental Learning Model from Logistic Regression Model Trained in Classification Learner

This example shows how to train a logistic regression model using Classification Learner. Then, at the command line, initialize and train an incremental model for binary classification using the information gained from training in the app.

Load the human activity data set. Randomly shuffle the data.

load(fullfile(matlabroot,'examples','stats','data','humanactivity.mat')) rng(1); % For reproducibility n = numel(actid); idx = randsample(n,n); X = feat(idx,:); actid = actid(idx);

For details on the data set, enter Description at the command line.

Responses can be one of five classes: Sitting, Standing, Walking, Running, or Dancing. Dichotomize the response by creating a categorical array that identifies whether the subject is moving (actid > 2).

moveidx = actid > 2; Y = repmat("NotMoving",n,1); Y(moveidx) = "Moving"; Y = categorical(Y);

Consider training a logistic regression model to about 1% of the data, and reserving the remaining data for incremental learning.

Randomly partition the data into 1% and 99% subsets by calling cvpartition and specifying a holdout (test) sample proportion of 0.99. Create variables for the 1% and 99% partitions.

cvp = cvpartition(n,'HoldOut',0.99); idxtt = cvp.training; idxil = cvp.test; Xtt = X(idxtt,:); Xil = X(idxil,:); Ytt = Y(idxtt); Yil = Y(idxil); 

### Train Model Using Classification Learner

Open Classification Learner by entering classificationLearner at the command line.

classificationLearner

Alternatively, on the Apps tab, under Machine Learning and Deep Learning, click the app icon.

Choose the training data set and variables.

1. On the Classification Learner tab, in the File section, click .

2. In the New Session dialog, under Data Set Variable, select the predictor variable Xtt.

3. Under Response, select From workspace and Ytt.

4. Under Validation, select No Validation.

5. Click .

Train a logistic regression model.

1. On the Classification Learner tab, in the Model Type section, click the arrow to expand the list of models. In the Logistic Regression Classifiers section, select Logistic Regression .

2. On the Classification Learner tab, in the Training section, select Train.

3. When Classification Learner finishes training the model, plot a confusion matrix. On the Classification Leaner tab, in the Plots section, select Confusion Matrix.

The confusion matrix suggests that the model classifies in-sample observations well.

Export the trained logistic regression model.

1. On the Classification Learner tab, in the Export section, select Export Model.

2. At the Export Model dialog, select OK.

Classification Learner passes the trained model, among other variables, in the structure array trainedModel to the workspace. You can dismiss Regression Learner .

### Initialize Incremental Model Using Exported Model

Extract the trained logistic regression model and the class names from trainedModel. The model is a GeneralizedLinearModel object. Because class names must match the data type of the response variable, convert the stored value to categorical.

Mdl = trainedModel.GeneralizedLinearModel; ClassNames = categorical(trainedModel.ClassNames);

Extract the intercept and the coefficients from the model. The intercept is the first coefficient.

Bias = Mdl.Coefficients.Estimate(1); Beta = Mdl.Coefficients.Estimate(2:end);

You cannot convert a GeneralizedLinearModel object to an incremental model directly. However, you can initialize an incremental model for binary classification by passing information learned from the app, such as estimated coefficients and class names.

Create an incremental model for binary classification directly. Specify the leaner, intercept, coefficient estimates, and class names learned from Classification Learner. Because good initial values of coefficients exist and all class names are known, specify a metrics warm-up period of length 0.

IncrementalMdl = incrementalClassificationLinear('Learner','logistic',... 'Beta',Beta,'Bias',Bias,'ClassNames',ClassNames,... 'MetricsWarmupPeriod',0)
IncrementalMdl = incrementalClassificationLinear IsWarm: 0 Metrics: [1×2 table] ClassNames: [Moving NotMoving] ScoreTransform: 'logit' Beta: [60×1 double] Bias: -471.7873 Learner: 'logistic' Properties, Methods

IncrementalMdl is an incrementalClassificationLinear model object for incremental learning using a logistic regression model. Because coefficients and all class names are specified, you can predict responses by passing IncrementalMdl and data to predict.

### Implement Incremental Learning

Use the updateMetricsAndFit function to perform incremental learning on the 99% data partition. Simulate a data stream by processing 50 observations at a time. At each iteration:

1. Call updateMetricsAndFit to update the cumulative and window classification error of the model given the incoming chunk of observations. Overwrite the previous incremental model to update the losses in the Metrics property. Note that the function does not fit the model to the chunk of data — the chunk is "new" data for the model.

2. Store the losses and the estimated coefficient β14.

% Preallocation nil = sum(idxil); numObsPerChunk = 50; nchunk = floor(nil/numObsPerChunk); ce = array2table(zeros(nchunk,2),'VariableNames',["Cumulative" "Window"]); beta14 = [IncrementalMdl.Beta(14); zeros(nchunk,1)]; % Incremental learning for j = 1:nchunk ibegin = min(nil,numObsPerChunk*(j-1) + 1); iend = min(nil,numObsPerChunk*j); idx = ibegin:iend; IncrementalMdl = updateMetricsAndFit(IncrementalMdl,Xil(idx,:),Yil(idx)); ce{j,:} = IncrementalMdl.Metrics{"ClassificationError",:}; beta14(j + 1) = IncrementalMdl.Beta(14); end

IncrementalMdl is an incrementalRegressionLinear model object that has experienced all the data in the stream.

Plot a trace plots of the performance metrics and β14.

figure; subplot(2,1,1) h = plot(ce.Variables); xlim([0 nchunk]); ylabel('Classification Error') legend(h,ce.Properties.VariableNames) subplot(2,1,2) plot(beta14) ylabel('\beta_{14}') xlim([0 nchunk]); xlabel('Iteration')

The cumulative loss gradually changes with iteration (chunk of 50 observations), whereas the window loss jumps. Because the metrics window is 200 by default, and updateMetrcisAndFit measures the performance every 4 chunks.

β14 adapts to the data as updateMetrcisAndFit processes chunks of observations.