Apache Mahout is a new open source project by the Apache Software Foundation (ASF) with the primary goal of creating highly scalable machine-learning algorithms that are fast and free to use under the Apache license. Mahout’s core algorithms for clustering, classification, and batch-based collaborative filtering are implemented on top of Apache Hadoop using the Mapreduce paradigm. Currently, Mahout supports mainly three common machine-learning use cases: (1) user-based recommendations, where data is mined using known user preferences and behaviours and used to predict new preferences for the user (there is also limited support for the related approach, item-based recommendations), (2) clustering looks for similarities between data points, using a user-specified metric, to identify clusters in the data, that is groups of points that appear more similar to each other than to members of other groups, and (3) classification applies discrete labels to data or predicts a continuous value (e.g., a price) based on previous examples of similar data.
What is Apache Mahout?
What are the features of Apache Mahout?
Can you briefly explain the Apache Mahout?
What does Apache Mahout do?
Can you explain Clustering in Mahout?
Can you explain how it is different from doing machine learning in R or SAS?
Can you explain Recommendation engine?
What are the machine learning algorithms supports in Apache Mahout?
Can you explain difference between Apache Mahout and Apache Spark’s MLlib?
Mention Some Use Cases Of Apache Mahout?