# Machine Learning

- [About Spark MLlib](https://www.sparkitecture.io/machine-learning/about-spark-mllib.md): MLlib is Apache Spark's scalable machine learning library.
- [Classification](https://www.sparkitecture.io/machine-learning/classification.md)
- [Logistic Regression](https://www.sparkitecture.io/machine-learning/classification/logistic-regression.md)
- [Naïve Bayes](https://www.sparkitecture.io/machine-learning/classification/naive-bayes.md)
- [Decision Tree](https://www.sparkitecture.io/machine-learning/classification/decision-tree.md)
- [Random Forest](https://www.sparkitecture.io/machine-learning/classification/random-forest.md)
- [Gradient-Boosted Trees](https://www.sparkitecture.io/machine-learning/classification/gradient-boosted-trees.md)
- [Regression](https://www.sparkitecture.io/machine-learning/regression.md)
- [Linear Regression](https://www.sparkitecture.io/machine-learning/regression/linear-regression.md)
- [Decision Tree](https://www.sparkitecture.io/machine-learning/regression/decision-tree.md)
- [Random Forest](https://www.sparkitecture.io/machine-learning/regression/random-forest.md)
- [Gradient-Boosted Trees](https://www.sparkitecture.io/machine-learning/regression/gradient-boosted-trees.md)
- [MLflow](https://www.sparkitecture.io/machine-learning/mlflow.md): MLflow is an open source library by the Databricks team designed for managing the machine learning lifecycle. It allows for the creation of projects, tracking of metrics, and model versioning.
- [Feature Importance](https://www.sparkitecture.io/machine-learning/feature-importance.md)
- [Model Saving and Loading](https://www.sparkitecture.io/machine-learning/model-saving-and-loading.md)
- [Model Evaluation](https://www.sparkitecture.io/machine-learning/model-evaluation.md)


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