ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...

Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important: BCE weighs mistakes from both ...

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

Abstract: The F1 score has been widely used to measure the performance of machine learning models. However, it is variant to the ratio of the positive class in the training data, π. Depending on how ...

Abstract: Efficient and accurate small molecule classification methods can significantly improve the efficiency of scientific research and industrial applications, but in real scenarios, many datasets ...

i am running binary classification report. my "target" column is binary 0,1 values, "pred_lablel" is binary 01, values and "prediction" is probabilities between 0-1 i get auc/roc, log loss but ...

Binary options let investors predict asset price movements for a fixed payout. Investors know potential gain or loss upfront, simplifying risk management. Example: Predicting a stock price increase ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The application of artificial neural network (ANN) techniques to spectroscopy has ...

Physical frailty is a pressing public health issue that significantly increases the risk of disability, hospitalization, and mortality. Early and accurate detection of frailty is essential for timely ...

Revised: This Reviewed Preprint has been revised by the authors in response to the previous round of peer review; the eLife assessment and the public reviews have been updated where necessary by the ...