Machine Learning

Machine learning

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.

Dimensionality Reduction for Visualization and Prediction

Dimensionality Reduction for Visualization and Prediction

0

Dimensionality reduction is primarily used for exploring data and for reducing the feature space in machine learning applications. In this post, I investigate techniques such as PCA to obtain insights from a whiskey data set and show how PCA can be used to improve supervised approaches. Finally, I introduce the notion of the whiskey twilight zone.

Interpreting Generalized Linear Models

Interpreting Generalized Linear Models

0

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.

Finding a Suitable Linear Model for Ozone Prediction

Finding a Suitable Linear Model for Ozone Prediction

0

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.