Binary Classification

This section presents binary classification applications, a type of supervised learning where a model is trained on labeled data to categorize data points into one of two distinct classes. Supervised learning involves providing the model with input data and corresponding labels during training, enabling it to learn patterns and make predictions on unseen data once trained.

In geosciences, binary classification can be applied to tasks such as:

• identifying mineralized zones versus barren zones

• detecting the presence or absence of specific geological features

• classifying areas as high or low risk for natural hazards.

These techniques help geoscientists make informed decisions and extract valuable insights from complex datasets.

• Weight of Evidence