Archives for uncertainty in machine learning
One of the main advantages of Dempster-Shafer theory is that we can utilize it for generating a degree of belief by taking all the evidence into account
We can leverage alternate data such as auto sales to help predict the spending propensity across different customer segments.
Machine learning heavily relies on probability theory. Hence, managing uncertainty (read imperfect or incomplete information) is key to machine learning (ML) projects. Ideally, deep learning makes it possible to produce dependable predictions on data from the same distribution the models were trained on. However, there are often disparities in the distribution of data on which […]