Could training data be smaller than evaluation data to force a model to learn at higher rates via hyperparameter tuning, as in self-optimizing knowledge-based models?
The proposal to use a smaller training dataset than an evaluation dataset, combined with hyperparameter tuning to “force” a model to learn at higher rates, touches on several core concepts in machine learning theory and practice. A thorough analysis requires a consideration of data distribution, model generalization, learning dynamics, and the goals of evaluation versus
Since the ML process is iterative, is it the same test data used for evaluation? If yes, does repeated exposure to the same test data compromise its usefulness as an unseen dataset?
The process of model development in machine learning is fundamentally iterative, often necessitating repeated cycles of model training, validation, and adjustment to achieve optimal performance. Within this context, the distinction between training, validation, and test datasets plays a major role in ensuring the integrity and generalizability of the resulting models. Addressing the question of whether
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
What is the goal of k-means clustering and how is it achieved?
The goal of k-means clustering is to partition a given dataset into k distinct clusters in order to identify underlying patterns or groupings within the data. This unsupervised learning algorithm assigns each data point to the cluster with the nearest mean value, hence the name "k-means." The algorithm aims to minimize the within-cluster variance, or