November 2017
Beginner to intermediate
366 pages
7h 59m
English
Content preview from R Data Analysis Projects
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Train model
We have our data split into the following:
- train: The user matrix we will use to build our recommendation model.
- test.known: We will feed this for our predict method along with our model. The output can now be compared to our test dataset.
- test: The test dataset is used for evaluating our model
Using these, let us go ahead and build our recommender system. For the first model, we are going to build a random model.
Building the random model looks as follows:
> random.model <- Recommender(train, "RANDOM")> random.modelRecommender of type 'RANDOM' for 'realRatingMatrix' learned using 1350 users.> getModel(random.model)$range[1] -5.998287 5.224277$labels [1] "j1" "j2" "j3" "j4" "j5" "j6" "j7" "j8" "j9" "j10" "j11" "j12" "j13" "j14" ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.