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.

An Introduction to Forecasting

An Introduction to Forecasting

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Forecasting is a powerful technique for time-series data. Here, I investigate the most common variants of forecasting algorithms: ARMA, ARIMA, SARIMA, and ARIMAX, which are primarily based on autocorrelation and moving averages.

Prediction vs Forecasting

Prediction vs Forecasting

Predictions do not always concern the future ...

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Prediction and forecasting are similar, yet distinct areas for which machine learning techniques can be used. Here, I differentiate the two approaches using weather forecasting as an example.

Performance Measures for Multi-Class Problems

Performance Measures for Multi-Class Problems

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For multi-class prediction scenarios, we can use similar performance measures as for binary classification. Here, I explain how we can obtain the (weighted) accuracy, micro- and macro-averaged F1-scores, and a generalization of the AUC to the multi-class setting.

Performance Measures for Model Selection

Performance Measures for Model Selection

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One of the main criteria indicating the quality of a machine learning models is its predictive performance. However, suitable performances measures differ depending on the prediction task. This post investigates the most commonly used quantities that are used for selecting regression and classification models.

Dimensionality Reduction for Visualization and Prediction

Dimensionality Reduction for Visualization and Prediction

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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.