  As machine learning becomes more and more available to the general public,
theoretical questions are turning into pressing practical issues. Possibly, one
of the most relevant concerns is the assessment of our confidence in trusting
machine learning predictions. In many real-world cases, it is of utmost
importance to estimate the capabilities of a machine learning algorithm to
generalize, i.e., to provide accurate predictions on unseen data, depending on
the characteristics of the target problem. In this work, we perform a
meta-analysis of 109 publicly-available classification data sets, modeling
machine learning generalization as a function of a variety of data set
characteristics, ranging from number of samples to intrinsic dimensionality,
from class-wise feature skewness to $F1$ evaluated on test samples falling
outside the convex hull of the training set. Experimental results demonstrate
the relevance of using the concept of the convex hull of the training data in
assessing machine learning generalization, by emphasizing the difference
between interpolated and extrapolated predictions. Besides several predictable
correlations, we observe unexpectedly weak associations between the
generalization ability of machine learning models and all metrics related to
dimensionality, thus challenging the common assumption that the \textit{curse
of dimensionality} might impair generalization in machine learning.
