Understanding Random Forests

Manish Kumar Barnwal explains how random forest algorithms work:

Say our dataset has 1,000 rows and 30 columns. There are two levels of randomness in this algorithm:

  • At row level: Each of these decision trees gets a random sample of the training data (say 10%) i.e. each of these trees will be trained independently on 100 randomly chosen rows out of 1,000 rows of data. Keep in mind that each of these decision trees is getting trained on 100 randomly chosen rows from the dataset i.e they are different from each other in terms of predictions.
  • At column level: The second level of randomness is introduced at the column level. Not all the columns are passed into training each of the decision trees. Say we want only 10% of columns to be sent to each tree. This means a randomly selected 3 column will be sent to each tree. So for the first decision tree, may be column C1, C2 and C4 were chosen. The next DT will have C4, C5, C10 as chosen columns and so on.

This  is a nice article and includes cases when not to use random forests.

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


June 2017
« May Jul »