Investigating London Crime Data

Carl Goodwin digs into London crime data by borough and sees if he can predict crime rates:

Optimal predictions sit close to, or on, the dashed line in the graphic below, i.e. where the prediction for each observation equals the actual. The Root Mean Squared Error (RMSE) measures the average differences, so should be as small as possible. And R-squared measures the correlation between prediction and actual, where 0 reflects no correlation, and 1 perfect positive correlation.

Our supervised machine learning outcomes from the CART and GLMmodels have weaker RMSEs, and visually exhibit some dispersion in the predictions at higher counts. Stochastic Gradient Boosting, Cubist and Random Forest have handled the higher counts better as we see from the visually tighter clustering.

It was Random Forest that produced marginally the smallest prediction error. And it was a parameter unique to the Random Forest model which almost tripped me up as discussed in the supporting documentation.

Also be sure to read his notebook to get the full story.  H/T R-Bloggers

Related Posts

AzureR Packages In Cran

David Smith points out that the Azure packages for R are now in CRAN: The suite of AzureR packages for interfacing with Azure services from R is now available on CRAN. If you missed the earlier announcements, this means you can now use the install.packages function in R to install these packages, rather than having to install from the […]

Read More

Solving Naive Bayes By Hand

I have a post that requires math and is meaner toward the Buffalo Bills than I normally am: Trust the ProcessThere are three steps to the process of solving the simplest of Naive Bayes algorithms. They are:1. Find the probability of winning a game (that is, our prior probability).2. Find the probability of winning given each input variable: whether Josh Allen starts the game, whether the team is […]

Read More


March 2018
« Feb Apr »