Decision trees Decision trees

The tree and the labyrinth

A decision tree is a machine learning model that is used to estimate a target variable based on several input variables. This target variable can be either numerical (regression trees) or nominal (classification trees). The methodology for constructing decision trees for regression and classification is described, as well as their interpretation.
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Decision trees Decision trees

The polysemy of Q

Cochran's Q is a widely used measure to detect heterogeneity between primary studies in a meta-analysis. Its statistical properties and hypothesis testing are reviewed. Finally, other measures calculated from this value are described, such as the I2 statistic and the H2 statistic, frequently used to quantify the intensity of heterogeneity between studies.
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Decision trees Decision trees

The art of resignation

The procedure for choosing the cut-off point for a diagnostic test is reviewed. To decide this threshold, which is influenced by the characteristics of the model and the clinical scenario in which it will be applied, we will take into account the sensitivity and precision of the test for each possible cut-off point. The precision enrichment ratio will be useful in cases with a large imbalance between the two diagnostic categories.
Read MoreThe art of resignation
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