Hi Laura, AIC (Akaike Information Criterion) can be used to evaluate and rank different models based on their performance for a given dataset. Here's how you can calculate AIC for the trained models:
- Load the models and data: Load the trained models (TrainedRegressionModel.mat) and some test data against which the models can be evaluted.
- Compute the log-likelihood: Use the predictions from the model (in this case predictFcn) and the ground truth values to calculate the log-likelihood for each model.
- Calculate AIC: Use the likelihood and number of parameters (k) to compute AIC as 2k – 2(log-likelihood)
Refer to the example code below for calculating AIC for a model
modelFile = 'TrainedRegressionModel.mat';
loadedData = load(modelFile);
trainedModel = loadedData.trainedModel;
T = yourTestData;
predictedY = trainedModel.predictFcn(T);
actualY = T.target; %% Assuming target contains the actual values
n = length(actualY);
residuals = actualY - predictedY;
sigma2 = var(residuals);
logLikelihood = -n/2 * log(2*pi) - n/2 * log(sigma2) - sum(residuals.^2) / (2*sigma2);
numPredictors = width(T)-1; %% excluding the response variable
k = numPredictors + 1;
AIC = 2*k - 2*logLikelihood;
Refer to following documentation for detailed instructions on making predictions using models trained in Regression Learner App:
