Imagine that you have built a predictive model with R and you would like to predict in real time, whether it is profitable to grant a loan and you wonder how to publish the script written in the R language as a Web Service.
According to Forrester, it costs 5x more to acquire new customers than it does to keep current ones. Another study shows that returning customers spend 30% more than new customers and are more willing to purchase new items from the offer of the vendor.
Among the many decisions you’ll have to make when building a predictive model is whether your business problem is either a classification or an approximation task. It’s an important decision because it determines which group of methods you choose to create a model: classification (decision trees, Naive Bayes) or approximation (regression tree, linear regression).
In this part of the tutorial you will learn more about definition and types of lift curves, accumulated LIFT with percentage scale how to construct a LIFT curve. You will gain more information about the accumulated LIFT with percentage scale and other types of LIFT curves.
The ROC curve is one of the methods for visualizing classification quality, which shows the dependency between TPR (True Positive Rate) and FPR (False Positive Rate).
Tutorial: How to establish quality and correctness of classification models? Part 3 – Confusion Matrix
Confusion Matrix is an N x N matrix, in which rows correspond to correct decision classes and the columns to decisions made by the classifier. The number ni,j at the intersection of i-th row and j-th column is equal to the number of cases from the i-th class which have been classified as belonging to the j-th class.
How to determine the quality and correctness of classification models? Part 2 – Quantitative quality indicators
In this part of tutorial we will discuss derived quality indicators and show how to select the appropriate indicator using as an example churn analysis.
Tutorial: How to determine the quality and correctness of classification models? Part 1 – Introduction
Classification is the process of assigning every object from a collection to exactly one class from a known set of classes.
Are you considering carrying out or outsourcing a data cleaning project? Find out what our experience tells us about this types of analyses.
One of the most typical tasks in machine learning is classification tasks. It may seem that evaluating the effectiveness of such a model is easy. Let’s assume that we have a model which, based on historical data, calculates if a client will pay back credit obligations.
In the previous post of our Understanding machine learning series, we presented how machines learn through multiple experiences. We also explained how, in some cases, human beings are much better at interpreting data than machines.