Category Statistics

Ensemble decision trees Ensemble decision trees

The wisdom of the weirdwoods

Simple decision trees have the problem of being less accurate than other regression or classification algorithms, as well as being less robust to small modifications of the data with which they are built. Some techniques for building ensemble decision trees are described, such as resampling aggregation (bagging) and random forests, which aim to improve the accuracy of predictions and avoid overfitting of models.
Ensemble decision trees Ensemble decision trees

The Alchemist

The Egger’s test is the most popular quantitative method to assess funnel plot asymmetry. It is based on a linear regression model between the effect measurement and the precision of the studies. A non-zero intercept value indicates asymmetry in the funnel plot probably due to a probable publication bias.
Ensemble decision trees Ensemble decision trees

A seance

The existence of publication bias can alter the results of a meta-analysis. The trim and fill method attempts to calculate an estimate of the effect corrected for bias that may have been introduced by missing studies. The objective is to impute these missing studies and include them in the funnel plot until the asymmetry disappears. Once this extended funnel is achieved, the effect measure is recalculated to obtain an estimate that corrects the effect of small studies.
Ensemble decision trees Ensemble decision trees

Behind the scenes

Shapiro-Wilk and Kolmogorov-Smirnov tests are the most commonly used contrast tests to check the goodness of fit to a normal distribution of our data. Its implementation is described step by step and its equivalence in graphical methods such as the theoretical quantile graph and the cumulative density function graph.
Ensemble decision trees Ensemble decision trees

The vanishing hitchhiker

The p curve focuses on the significant p values of the primary studies of a meta-analysis and allows us to estimate, in addition to publication bias, whether there may be a real effect after the study, what its magnitude is, and whether there is suspicion of improper practices by researchers to obtain statistically significant values.
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