Category Statistics

Overfitting Overfitting

Apophenia

Overfitting occurs when an algorithm over-learns the details of the training data, capturing not only the essence of the relationship between them, but also the random noise that will always be present. This negatively affects its performance and its ability to generalize when we introduce new data, not seen during training.
Overfitting Overfitting

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.
Overfitting Overfitting

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.
Overfitting Overfitting

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.
Overfitting Overfitting

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.
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