Measuring Model Accuracy

Fabio Veronesi shows several methods of testing model accuracy:

Mean Squared Deviation or Mean Squared Error

This is simply the numerator of the previous equation, but it is not used often. The issue with both the RMSE and the MSE is that since they square the residuals they tend to be more affected by large residuals. This means that even if our model explains the large majority of the variation in the data very well, with few exceptions; these exceptions will inflate the value of RMSE.

Click through for several calculations.  H/T R-bloggers

Related Posts

Tidy Anomaly Detection With Anomalize

Abdul Majed Raja walks us through an example using the anomalize package: One of the important things to do with Time Series data before starting with Time Series forecasting or Modelling is Time Series Decomposition where the Time series data is decomposed into Seasonal, Trend and remainder components. anomalize has got a function time_decompose() to perform the same. […]

Read More

Uploading Data Sets To Azure ML From R

Leila Etaati continues her series on the Azure ML R package by showing how to upload a data set: There is a function in AzureML package name “workspace” that creates a reference to an AzureML Studio workspace by getting the authentication token and workspace id as below: 1 ws <– workspace( id , auth  ) to […]

Read More

Categories

July 2017
MTWTFSS
« Jun Aug »
 12
3456789
10111213141516
17181920212223
24252627282930
31