Understanding Boosted Trees

Maria Jesus Alonso explains decision trees and their subsequent improvements:

Bagging (or Bootsrap Aggregating), the second prediction technique brought to the BigML Dashboard and API, uses a collection of trees (rather than a single one), each tree built with a different random subset of the original dataset for each model in the ensemble. Specifically, BigML defaults to a sampling rate of 100% (with replacement) for each model. This means some of the original instances will be repeated and others will be left out. Bagging performs well when a dataset has many noisy features and only one or two are relevant. In those cases, Bagging will be the best option.

Random Decision Forests extend the Bagging technique by only considering a random subset of the input fields at each split of the tree. By adding randomness in this process, Random Decision Forests help avoid overfitting. When there are many useful fields in your dataset, Random Decision Forests are a strong choice.

Click through for how boosted trees change this model a bit.

Related Posts

Multi-Threaded R With Microsoft R Client

David Parr shows us how to get started with Microsoft R Client and performs some quick benchmarking: This message will pop up, and it’s worth noting as it’s got some information in it that you might need to think about: It’s worth noting that right now Microsoft r Client is lagging behind the current R version, and […]

Read More

Image Clustering With Keras And R

Shirin Glander shows us how to use R to extract learned features from Keras and cluster those features: For each of these images, I am running the predict() function of Keras with the VGG16 model. Because I excluded the last layers of the model, this function will not actually return any class predictions as it would normally […]

Read More

Categories

April 2017
MTWTFSS
« Mar May »
 12
3456789
10111213141516
17181920212223
24252627282930