Evidence Based Medicine pills
Evidence Based Medicine pills
There are three important components involved in the training process of a machine learning algorithm: the loss function, the performance metric, and the validation control. The need to balance accuracy and predictive capacity to obtain robust and effective models is emphasized.
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.
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.
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.
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.