By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
During this useful booklet, 4 Cloudera info scientists current a collection of self-contained styles for appearing large-scale facts research with Spark. The authors deliver Spark, statistical equipment, and real-world information units jointly to coach you ways to process analytics difficulties by way of example.
You’ll commence with an creation to Spark and its environment, after which dive into styles that practice universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields resembling genomics, safety, and finance. when you have an entry-level figuring out of laptop studying and information, and also you software in Java, Python, or Scala, you’ll locate those styles invaluable for engaged on your personal information applications.
• Recommending track and the Audioscrobbler information set
• Predicting woodland hide with selection trees
• Anomaly detection in community site visitors with K-means clustering
• figuring out Wikipedia with Latent Semantic Analysis
• reading co-occurrence networks with GraphX
• Geospatial and temporal info research at the manhattan urban Taxi journeys data
• Estimating monetary hazard via Monte Carlo simulation
• studying genomics info and the BDG project
• examining neuroimaging facts with PySpark and Thunder
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Extra info for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
This name could be misspelled or nonstandard, and this may only be 40 | Chapter 3: Recommending Music and the Audioscrobbler Data Set detected later. For example, “The Smiths,” “Smiths, The,” and “the smiths” may appear as distinct artist IDs in the data set, even though they are plainly the same. txt, which maps artist IDs that are known misspell‐ ings or variants to the canonical ID of that artist. The Alternating Least Squares Recommender Algorithm We need to choose a recommender algorithm that is suitable for this implicit feed‐ back data.
XYT should still be as close to A as possible. After all, it’s all we’ve got to go on. It will not and should not reproduce it exactly. The bad news again is that this can’t be solved directly for both the best X and best Y at the same time. The good news is that it’s trivial to solve for the best X if Y is known, and vice versa. But, neither is known beforehand! 42 | Chapter 3: Recommending Music and the Audioscrobbler Data Set Fortunately, there are algorithms that can escape this catch-22 and find a decent solu‐ tion.
4% of the input pairs actually match. , many pairs of records will look like matches even though they actually are not). Summary Statistics for Continuous Variables Spark’s countByValue action is a great way to create histograms for relatively low car‐ dinality categorical variables in our data. But for continuous variables, like the match scores for each of the fields in the patient records, we’d like to be able to quickly get a basic set of statistics about their distribution, like the mean, standard deviation, and extremal values like the maximum and minimum.
Advanced Analytics with Spark: Patterns for Learning from Data at Scale by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills