Generalized linear models (GLMs) are related to conventional linear models but there are some important differences. For example, GLMs are based on the deviance rather than the conventional residuals and they enable the use of different distributions and linker functions. This post investigates how these aspects influence the interpretation of GLMs.
Machine learning is a field of artificial intelligence (AI) that is concerned with learning from data. Machine learning has three components:
- Supervised learning: Fitting predictive models using data for which outcomes are available.
- Unsupervised learning: Transforming and partitioning data where outcomes are not available.
- Reinforcement learning: on-line learning in environments where not all events are observable. Reinforcement learning is frequently applied in robotics.
Posts on machine learning
In the following posts, machine learning is applied to solve problems using R.
Although ordinary least-squares regression is often used, it is not appropriate for all types of data. Using the airquality data set, I try to find a generalized linear model that fits the data better. For this purpose, I use the following methods: weighted regression, Poisson regression, and imputation.
Linear machine learning models are very convenient for interpretation. This post discusses the following aspects: residuals, coefficients, standard errors, p-values, the F-statistic, and much more.
People without technical backgrounds can have a hard time understanding plots. A less formal means for conveying information is provided by infographics, which are easily understandable. This post compares several free tools for creating engaging infographics.