43. Introducing MLLib
INTRODUCING MLLIB
Movie recommendations using Spark's machine learning library
MLLIB Capabilities
Feature extraction
Term Frequency /Inverse Document Frequency useful for search
Basic statistics
Chi-squared test, Pearson or Spearman correlation, min, max, mean, variance
Linear regression, logistic regression
Support Vector Machines
Naives Bayes Classifier
Decision trees
K-Means clustering
Principal component analysis, singular value decomposition
Recommendations using Alternating Least Squares
Special MLLIB Data Types
Vector (dense or sparse)
LabeledPoint
Rating
For More Depth
I really like "Advanced Analytics with Spark" from O'Reilly
Let's Make Some Movie Recommendations
IT'S JUST THAT EASY ... Let's run it.
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