A fundamental pillar of statistical learning is the concept of out-of-sample prediction. This differentiates it from classical statistics where emphasis might be more on understanding relationships within a given set of data.An Introduction to Statistical Learning: with Applications in R by Gareth James
At a conceptual level,bootstrap involves repeatedly extracting samples of size n from an (originally) observed dataset, and computing the test statistic on each copy of the sample.An Introduction to Statistical Learning: with Applications in R by Gareth James
It is a fact that many of the concepts that underpin modern statistical learning were developed in other disciplines - such as computer science, engineering, and physics - before gaining popularity in statistics.An Introduction to Statistical Learning: with Applications in R by Gareth James
In a regression setting, interpretability of results involves assessing the likely impact of a unit change in one of the inputs on the output, while holding all other inputs fixed.An Introduction to Statistical Learning: with Applications in R by Gareth James
There is no free lunch in statistics: in general, no one procedure outperforms all others under all possible situations.An Introduction to Statistical Learning: with Applications in R by Gareth James
The term machine learning is sometimes used to refer to a subset of statistical learning methods which emphasizes large-scale, complex computational algorithms.An Introduction to Statistical Learning: with Applications in R by Gareth James
In statistics and machine learning, a set of observations will usually not follow exactly a linear, quadratic, or some other simple relationship.An Introduction to Statistical Learning: with Applications in R by Gareth James
A trained human eye is fairly good at automatically deciphering patterns within data.An Introduction to Statistical Learning: with Applications in R by Gareth James
Statistical learning refers to a vast set of tools for understanding data. These tools can be classified as supervised or unsupervised.An Introduction to Statistical Learning: with Applications in R by Gareth James
Cross-validation is primarily a way of measuring the predictive performance of a statistical model or a machine learning algorithm.An Introduction to Statistical Learning: with Applications in R by Gareth James
Typically, there will not be a unique way to perform the loss function averaging process; we can average directly, or we can perform some form of weighting.An Introduction to Statistical Learning: with Applications in R by Gareth James