Explore the methods for preventing selection bias when we conduct statistical analysis.
Gabriele De Luca
Gabriele specializes in artificial intelligence and innovation and on the impact of technology on society. He has authored several scientific papers in the sectors of machine learning, natural language processing, network theory, and multi-agent simulations. On Baeldung he contributes to the section on computer science, where he publishes articles on the theory behind machine learning and artificial intelligence.
Here's what I've written (so far):
Explore the most common techniques for feature selection and reduction for text classification.
Explore how to inflate or deflate a polygon utilizing homothety and offsetting.
Explore the components, representations, and applications of finite-state machines.
Explore the relationship between the number of support vectors and the performances of a support vector classifier.
Learn a conceptual difference between methods and functions.
Explore the Ackermann function and the problems associated with its computation.
Explore numeral systems and their associated concepts.
Learn about the ugly duckling theorem in its relationship with algorithmic bias.
Learn how to normalize the features of a table or dataset.