R for applications in data science

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All posts with the R tag deal with applications of the statistical programming language R in the data science setting.

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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.

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.

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.

Radar plots

Radar plots

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Radar plots are exceptional for visualizing the properties of individual objects. Here, I demonstrate how to draw radar plots in R by plotting the properties of whiskeys from several distilleries.

Interpreting Generalized Linear Models

Interpreting Generalized Linear Models

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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.