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

Testing Spatial Equilibrium Concepts With tidycensus

Ignacio Sarmiento Barbieri walks us through the concept of spatial equilibrium and tests using data from the tidycensus package: Let’s take the model to the data and reproduce figures 2.1. and 2.2 of “Cities, Agglomeration, and Spatial Equilibrium”. The focus are two cities, Chicago and Boston. These cities are chosen because both differ in how easy […]

Read More

Interacting With SQL Server From Pandas

Tomaz Kastrun shows how to use pyodbc to interact with a SQL Server database from Pandas: In the SQL Server Management Studio (SSMS), the ease of using external procedure sp_execute_external_script has been (and still will be) discussed many times. But the reason for this short blog post is the fact that, changing Python environments using Conda package/module management within Microsoft […]

Read More


April 2017
« Mar May »