Prerequisites - OBS, important Flashcards
What is statistical inference? How does it differ from descriptive statistics?
Statistical Inference: Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability.[1] Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population.
Descriptive statistics: Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population
What is Machine Learning Inference?
Machine learning (ML) inference involves applying a machine learning model to a dataset and generating an output or “prediction”. This output might be a numerical score, a string of text, an image, or any other structured or unstructured data.
How do statistics and ML relate to inference and predictions?
Many methods from statistics and machine learning (ML) may, in principle, be used for both prediction and inference.
However, statistical methods have a long-standing focus on inference, which is achieved through the creation and fitting of a project-specific probability model.
By contrast, ML concentrates on prediction by using general-purpose learning algorithms to find patterns in often rich and unwieldy data. (wide data, rather than long data)
