Evidence Based Medicine pills
Evidence Based Medicine pills
Principal component analysis (PCA) is a statistical dimensionality reduction technique that transforms correlated variables into independent orthogonal components. Its purpose is to simplify complex data structures by maximizing explained variance and eliminating informational redundancy through methods such as singular value decomposition.
The post analyzes the problem of class imbalance in biomedical models and how overall accuracy can become useless when the minority class is the clinically relevant one. It explains which evaluation metrics are most appropriate and outlines the main strategies to handle imbalance, such as oversampling (SMOTE, ADASYN), selective undersampling (Tomek links), and ensemble methods that stabilize performance in low-prevalence scenarios.
The rationale for minimizing the sum of squared errors in linear regression, which is often presented as a simple choice of convenience, is discussed. A probabilistic perspective suggests that the least squares equation arises naturally from assuming that the model's residuals follow a normal distribution.
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.