4.3. Adaptive Functions
In supervised learning, the adaptive function refers to the ability of the model to adjust its parameters in response to new input data. Specifically, it refers to the ability of the model to learn from the labelled training data and improve its performance on new/unseen data. During the training phase, the model is presented with a set of input features and the corresponding output labels, and it adjusts its parameters (e.g. weights and biases) to minimize the error between its predicted outputs and the true labels. This process of updating the model’s parameters is often referred to as adaptation. The goal of this learning process is for the model to be able to accurately predict the output for input data. This is known as the model’s generalization performance, and it is a key measure of its adaptive function.
We provide ready-to-use adaptive function models in MLPro-SL’s pool of objects, which can be found, as follows:
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