# Classification ## Description: Classification algorithms are used to identify into which classes observations of data should fall. This problem could be considered part of pattern recognition in that we use training data (historical information) to recognize patterns to predict where new data should be categorized. ### Common Use Cases: * Fraudulent activity detection * Loan default prediction * Spam vs. ham * Customer segmentation * Benign vs. malignant tumor classification * and many more... ### Classification Algorithms included in MLlib: * [Logistic regression](/machine-learning/classification/logistic-regression.md) (both binomial and multiclass) * [Decision trees](/machine-learning/classification/decision-tree.md) * [Random forests](/machine-learning/classification/random-forest.md) * Gradient-boosted trees * Multilayer perceptron * Linear Support Vector Machine * One-vs-Rest classifier (a.k.a. One-vs-All) * [Naïve Bayes](/machine-learning/classification/naive-bayes.md) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://www.sparkitecture.io/machine-learning/classification.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.