By Anup Nair
Through this post I would like to describe a package that I recently developed and published on CRAN. The package titled IMP (Interactive Model Performance) enables interactive performance evaluation & comparison of (binary) classification models.
There are a variety of different techniques available to assess model fit and to evaluate the performance of binary classifiers. As we would expect, there are multiple packages available in R that could be used for this purpose. For instance, the ROCR package is an excellent choice for computing and plotting a range of different performance measures for classification models. The general purpose caret package also provides various options for assessing model fit and evaluating model performance.
While I continue to use these packages, the idea behind trying to create something on my own was triggered by the following considerations:
- Accelerate the model building and evaluation process – Partially automate some of the iterative, manual steps involved in performance evaluation and model fine-tuning by creating small, interactive apps that could be launched as functions (The time saved can then be more effectively utilized elsewhere in the model building process)
- Enable simultaneous comparison of multiple models – Performance evaluation almost always entails comparing the performance of multiple candidate models in an attempt to select a “best” model (basis some definition of what qualifies as “best”). The intent was to write functions that are inherently designed to take multiple model output as arguments, and perform model evaluations simultaneously on them.
- Visualization (using
ggplot2) – Related to the point above; Functions were designed to generate visualizations enabling quick performance comparison of multiple models
Let us now look at some of the key functions from the package and few of the functionalities that it provides:
Analyze Confusion Matrix interactively
Creating a confusion matrix is a common technique to assess the performance of a classification model. The …read more
Source:: r-bloggers.com