Building predictive models in AdvancedMiner

Tutorial: Building predictive models in AdvancedMiner

In this tutorial you will learn how to build a predictive model using AdvancedMiner in a few simple steps. First of all, go to the free AdvancedMiner download page.

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R SaaS

Tutorial: How to publish script in R as a Web Service?

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.

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Lead scoring

How to run a successful sales campaign to existing customers

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.

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Na czym polega aproksymacja i klasyfikacja

Approximation or Classification – which one to choose?

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).

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Krzywa LIFT

Tutorial: How to establish quality and correctness of classification models? Part 5 – Lift curve

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.

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How to assess quality and correctness of classification models? Part 4 – ROC Curve

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).

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Confusion Matrix - Macierz Błędów

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.

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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.

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Quality and correctness of classification models

Tutorial: How to determine the quality and correctness of classification models? Part 1 – Introduction

What is classification?

Classification is the process of assigning every object from a collection to exactly one class from a known set of classes.

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Data Quality Projects

What is data quality all about and how to run a data cleaning project?

Are you considering carrying out or outsourcing a data cleaning project? Find out what our experience tells us about this types of analyses.

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Understanding machine learning #3: Confusion matrix – not all errors are equal

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. 

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Machine learning

Understanding machine learning #2: Do we need machine learning at all?

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. 

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