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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.
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.
Decision trees Decision trees

The Palace of Probabilities

With small sample size, the variance of the Pearson’s coefficient increases, decreasing the precision of its estimates. The use of Fisher's z helps to stabilize the variance and obtain more precise estimates in meta-analyses whose outcome measure is a correlation, and when the sample of primary studies is small.
Decision trees Decision trees

An epic dance

Multiple regression regularization (shrinkage) techniques can be very useful to address collinearity or overfitting problems. In addition, they can be used to select the independent variables and reduce multidimensionality, achieving more robust and easy-to-interpret models. Ridge, lasso and elastic network regression techniques are described.
Decision trees Decision trees

The paradox of the air

The parameters that report on the quality of a multiple logistic regression model and that are usually provided by the statistical programs with which they are carried out are reviewed. Emphasis is placed on the goodness of fit, the predictive capacity and the statistical significance of the model.
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