Introduction to MLlib
MLlib is Spark's scalable machine learning library. It provides common algorithms and utilities for classification, regression, clustering, and collaborative filtering, designed to run on large datasets across a cluster.
MLlib is the machine learning component of Apache Spark. It uses the DataFrame‑based API (ml package) which is the recommended approach.
Why MLlib?
- Scalable – handles terabytes of data.
- Integrated with Spark pipelines, SQL, and DataFrames.
- Includes feature transformers, estimators, and evaluators.
- Supports distributed training.
MLlib Ecosystem
MLlib components:
- Transformers: convert one DataFrame to another (e.g., feature scaling, one‑hot encoding).
- Estimators: fit on data to produce a transformer/model (e.g., `LogisticRegression`, `RandomForestClassifier`).
- Pipelines: chain multiple transformers and estimators into a single workflow.
- Evaluators: compute metrics like accuracy, RMSE.
Importing MLlib
from pyspark.ml.feature import VectorAssembler, StringIndexer, StandardScaler
from pyspark.ml.classification import LogisticRegression
from pyspark.ml import Pipeline
from pyspark.ml.evaluation import BinaryClassificationEvaluatorTwo Minute Drill
- MLlib is Spark's distributed machine learning library.
- Uses DataFrame‑based API (ml package).
- Includes transformers, estimators, pipelines, and evaluators.
- Works seamlessly with large‑scale data.
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