spark-ml-source-analysis

Why we included this project

Most Spark ML users call fit() and trust the defaults. This Chinese-language guide is for the ones who want to see what happens inside: it pairs the math behind each algorithm with a close read of the Scala code, covering k-means, decision trees, logistic regression, ALS, and SVD/PCA as they are actually distributed across a cluster. It is not a library you install and call; it is a reading companion. Teams already on Spark will find it useful when a model misbehaves, when defaults need tuning, or when a built-in estimator must be extended. One honest caveat: the walkthroughs target Spark 1.6.1, with parts of 2.x, so treat the implementation detail as history and check against your current version. The same structure works as a syllabus for self-study or for bringing a team up to speed on distributed ML.