I have a fairly small dataset of 225 points. I have a target (labelled numeric), a feature (normalised numeric) and a quality index with set of normalised weights that describe the likelihood that the observation will contribute to a linear relationship between target and feature. Good data for weighted linear regression.
The dataset is currently at only 225 points but it's growing so I'd like to explore DNNs to improve on the performance of the weighted linear approach. Can Keras do this?
What I am curious about is the possibility that the weight optimization process in the DNN training could itself eliminate points with a low quality index, i.e. those points that do not contribute to a linear fit and result in a high loss function return.
But I am very new to deep learning so my understanding could be wrong.