KNIME Press

This book provides an overview of scoring metrics to evaluate a classification and regression model. It covers confusion matrices and class statistics, numeric scoring metrics and visual scoring techniques, imbalanced target classes, resampling, and Cohen’s kappa. The authors also examine how coefficients of a logistic regression model can be interpreted as percentage effects. The book demonstrates the different techniques based on various practical scenarios, such as credit scoring and credit card fraud detection.

A detailed overview of the main tools in KNIME Analytics Platform, providing an ideal starting point for those who want to begin working with KNIME. The goal is to give new KNIME users the necessary knowledge to start analyzing, manipulating, and reporting complex data. No previous knowledge of KNIME is required.
This book has been updated for KNIME Analytics Platform 4.3.

This book is the sequel to the introductory book KNIME Beginner's Luck. Building upon the reader's first experience with KNIME, this book presents some more advanced features, like looping, selecting workflow paths, flow variables, reading and writing data from and to a database, accessing REST services and Google Sheets, and more. The goal of this book is to elevate your data analysis from a basic exploratory level to a more professionally organized and complex structure. This book has been updated for KNIME Analytics Platform 4.3.

This book extends the catalogue of KNIME Press books with a description of techniques to access, process, and analyze text documents using the KNIME Text Processing extension. The book covers text data access, text pre-processing, stemming and lemmatization, enrichment via tagging, bag of words and keyword extraction, term frequencies, word vectors to represent text documents, and finally topic detection and sentiment analysis. Some basic knowledge of KNIME Analytics Platform is required. This book has been updated for KNIME Analytics Platform 4.0.

There are many declinations of data science projects: with or without labeled data; stopping at data wrangling or involving machine learning algorithms; predicting classes or predicting numbers; with unevenly distributed classes, with binary classes, or even with no examples of one of the classes; with structured data and with unstructured data; using past samples or just remaining in the present; with real time or close to real time execution requirements and with acceptably slower performances; showing the results in shiny reports or hiding the nitty and gritty behind a neutral IT architecture; and - last but not least - with large budgets or no budget at all.

Data blending is a very big part of the sexiest job of the 21st century, including data source blending, data type blending, database blending, time blending , and tool blending. In order to help with all specific blending requests, in November 2016 we started a blog post series with the title “Will they blend?”. Each post faces a blending challenge and offers a solution.

This easy to follow guide can help your transition from SAS Base to KNIME by taking you through the steps you'd take in SAS, and how you'd do the same thing in KNIME Analytics Platform. This includes reading data, grouping and sorting, PROC SQL, statistics, join and concatenate, string operations, mathematical formulas, reporting, and more. No previous knowledge of KNIME is required.

This easy to follow guide can help you transition from Excel to KNIME. It maps the most commonly used Excel functions and techniques to their KNIME equivalents, taking you through the steps you’d take in Excel and showing you how they can be done in KNIME Analytics Platform. Find out, for example, how data reading, filtering, sorting, pivoting, math formulas, and commonly used functions such as vlookup are handled in KNIME. No previous knowledge of KNIME is required.

This guide will help you transition from Alteryx to KNIME. It maps the most commonly used Alteryx functions and techniques to their KNIME equivalents: from importing data, to manipulating data, to documenting your workflow, through to modeling and machine learning. No previous knowledge of KNIME is required.