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.
“If (there) was one thing all people took for granted, (it) was conviction that if you feed honest figures into a computer, honest figures (will) come out. Never doubted it myself till I met a computer with a sense of humor.” ― Robert A. Heinlein, The Moon is a Harsh Mistress
Information about provided services, customers and transactions can be stored in different database systems and data warehouses, depending on the way in which a company operates.
Due to such arrangements, even the simplest analyses or report may require significant expenditures of time, as well as in-depth knowledge about database systems and their availability.
A popular phrase tossed around when we talk about statistical data is “there is correlation between variables”. However, many people wrongly consider this to be the equivalent of “there is causation between variables”. It’s important to explain the distinction:
As Predictive Analytics (also called Data Mining or Data Science) is gaining momentum and spreading across companies and sectors, we have created a short guide to some common terms in this field. We hope you like it!
In the previous post we presented a few methods of data analysis, which are used to identify customer needs and preferences and allow us to predict their behavior. Such knowledge results in building better marketing and sales offers which meet specific customer expectations. In today's article we present further examples of Data Mining methods that can be applied in daily business operations.
The key asset of any company is its customers. Therefore, it is crucial to identify their needs and preferences as well as to know the factors affecting their behavior. The collected customer data allows predicting customer behavior and creating appropriate marketing offers, sales plans, and retention programs that match customers' needs.
On April 20, in the BIG Data Awards Gala, we received a prize in the BIG DATA Congress: Think Big CEE competition for Polish Innovation. The award in this category is given to outstanding Polish companies for introducing groundbreaking technologies and for having real impact on the shaping of the market.