Machine learning library also called the “SparkML” or “MLLib” comprises of basic learning algorithms, including classification, regression, clustering and collaborative filtering.
Spark is turning into the de-facto platform for building machine learning algorithms and applications. The developers work on Spark for implementing machine algorithms in a scalable and concise manner in the Spark framework. We will become familiar with the concepts of Machine learning, its uses and algorithms with this framework. Agile always opts for a framework, which delivers short and quick results.
ML Algorithms incorporate basic learning algorithms such as classification, regression, clustering and collaborative filtering.
It incorporates feature extraction, transformation, dimension reduction and selection.
Pipelines provide tools for developing, evaluating and tuning machine-learning pipelines.
Following are a few popular algorithms −
A recommendation system is a subclass of information filtering system that seeks prediction of “rating” and “preference” that a user suggests to a given thing.
Recommendation system includes various filtering systems, which are utilized as follows −
It incorporates building a model based on the past behavior as well as similar decisions made by other users. This specific filtering model is utilized to predict items that a user is interested to take in.
It includes the filtering of discrete characteristics of an item in order to recommend and add new items with comparable properties.
In our subsequent chapters, we will concentrate on the utilization of recommendation system for solving a specific problem and improving the prediction performance from the agile methodology perspective.