Planning For Learning

Data collection for machine learning may be complex or costly. In this example we assume we have a component that provide data goals (i.e., data deemed most useful to a learning component). We then use a planner to collect data for the learner and evaluate the models it can produce via cross-validation. If the performance of the model is good enough we stop.

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