- Regression Learner partitions the test data randomly. In Classification Learner, the partition is random and stratified. (https://www.mathworks.com/help/stats/cvpartition.html). Stratification is based on the class labels. That is, an attempt is made to keep the class frequency similar in the training and test sets. If you want to control your test partition, you could (1) first partition your data into train and test outside of the Learner app, (2) load the training data into the Learner app at the session start dialogue, and (3) later load the separate test data into the Learner app.
- You can use model-agnostic interpretability techniques such as Partial Dependence Plot (PDP), Shapley, and LIME on your GPR models. In R2023b, you can use these techniques inside the Learner app using the "Explain" tab within the Learner app.
Optimizing Interpretability in Gaussian Process Regression Models: A Strategic Approach to Preprocessing and Testing Data
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Hi
I am utilizing the Regression Learner App to develop a model that can adjust my RAW data so that it can accurately predict data accordingly. My question pertains more to the general usage of the tool.
1. When setting my input data, there is an option to reserve a portion of the data for testing. Does this process allocate the learning and testing data randomly, or does it do so sequentially, e.g., using the first few weeks of data for training and the remaining for testing?
2. I have discovered that Gaussian Process Regression (GPR) models yield the best results for my dataset. However, this type of model lacks interpretability. My inputs include Signal Data, Temperature, and Humidity.
If I wish to assess the individual impact of each input on the overall signal, in terms of applying a linear or polynomial correction before the GPR model processing, is this possible? By doing so, I can minimize the amount of data fed into the GPR model, which in turn might provide some interpretability for my overall modeling process.
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Drew
on 3 Nov 2023
If this answer helps you, please remember to accept the answer.
Example screenshot from the Regression Learner app, within the Explain tab, for a GPR model on fisheriris data:
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