# About Spark MLlib MLlib works with Spark's APIs and with NumPy in Python and with R libraries. Since Spark excels at iterative computation, MLlib runs very fast with highly-scalable, high-quality algorithms that leverage iteration. ### Included Functionality: #### ML algorithms include: * Classification: logistic regression, naive Bayes,... * Regression: generalized linear regression, survival regression,... * Decision trees, random forests, and gradient-boosted trees * Recommendation: alternating least squares (ALS) * Clustering: K-means, Gaussian mixtures (GMMs),... * Topic modeling: latent Dirichlet allocation (LDA) * Frequent itemsets, association rules, and sequential pattern mining #### ML workflow utilities include: * Feature transformations: standardization, normalization, hashing,... * ML Pipeline construction * Model evaluation and hyper-parameter tuning * ML persistence: saving and loading models and Pipelines #### Other utilities include: * Distributed linear algebra: SVD, PCA,... * Statistics: summary statistics, hypothesis testing,... ### Resources * [Spark MLlib Website](https://spark.apache.org/mllib/) * [Getting Starting Guide](https://spark.apache.org/docs/latest/ml-guide.html) --- # 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/about-spark-mllib.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.