November 2018
Beginner to intermediate
354 pages
6h 59m
English
Content preview from Hands-On Geospatial Analysis with R and QGIS
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Random forest
With random forest classification, we build a number of decision trees on training samples (sampling with replacement), and for each split on the tree, a random sample of predictors is chosen. Random forests generally perform very well with classifications. But, for our case, as we have only two predictors, we will not see any improvement. In practical cases, if we took more predictors of landslides, such as the normalized difference vegetation index (NDVI), aspect, and so on, this algorithm would show (for most cases) improved performance.
Similar to what we did with CART, we build the model on the training dataset and test its accuracy on the test dataset. Now, we build a random forest using the randomForest() function of ...
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.