Posts

Inference vs Prediction

Inference vs Prediction

Generative modeling or predictive modeling?

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Inference is concerned with learning about the data generation process, while prediction is concerned with estimating the outcome for new observations. These contrasting principles are associated with the the generative modeling and machine learning communities. Here, I showcase the differences and similarities between the two concepts and offer insights about what the practitioners from both fields can learn from each other.

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.

Statistical Nomenclature for Variables

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Variables can be identified by their value as well as their role. Variables are categorized into quantitative, categorical, and ordinal variables, depending on their values. Moreover, when variables are used in statistical models, additional terms are used to indicate their role such as dependent, independent, and confounding variable.