Category Machine learning

Machine learning, Sin categoría, Statistics

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.

Manuel Molina
11/27/2024

Machine learning, Statistics

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.

Manuel Molina
11/26/2024

Machine learning, Statistics

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.

Manuel Molina
05/21/2024

Epidemiology, Machine learning

An intruder from another world

The F1-score, also called F-score or F-measure, is an estimator of the classification capacity of a test that is frequently used in data science and artificial intelligence algorithms and that can be useful for evaluation of diagnostic tests. It is the harmonic mean of sensitivity and positive predictive value, so it weights the value of both in a single estimator.

Manuel Molina
11/06/2023

Critical appraisal, Machine learning

The intelligent decalogue

The aspects that must be assessed for the critical appraisal of documents that use machine learning techniques are reviewed, including the selection of participants, the treatment of the data during the development of the model and its final implementation in clinical practice.

Manuel Molina
06/20/2023