• The Precision/Recall Trade-off

  Precision and recall are two commonly used evaluation metrics for binary classification models, and the trade-off between them refers to the tension between these two measures. Precision measures the fraction of positive predictions that are actually correct, while recall measures the fraction of actual positive cases that are correctly identified by the model. Precision and recall are often in conflict because increasing precision typically leads to a decrease in recall, and vice versa. In oFew readers

• Classification Types

  Classification is the task of categorizing data into a relatively small set of classes. There are broadly two different classification types: Binary Classification Multiclass Classification Binary Classification Binary classification is a type of supervised machine learning problem where the goal is to predict one of two possible outcomes for a given input data. The two possible outcomes are typically represented as 0 (negative class) and 1 (positive class), although any binary labelinFew readers