Author: Paolo Tamagnini (KNIME)

One of the key challenges of utilizing supervised machine learning for real world use cases is that most algorithms and models require lots of data with quite a few specific requirements. 

You have just downloaded KNIME Analytics Platform, what next?

Author: Rosaria Silipo (KNIME)

Author: Martyna Pawletta (KNIME)

We are often asked if it’s possible to work with ontologies in KNIME Analytics Platform.

With “work with ontologies” people can mean many different things but let's focus today on one particular ontology and basic tasks including reading and querying ontologies to create an interactive tool at the end. For this purpose, today, we dive into the world of chemistry to use the ChEBI ontology (Chemical Entities of Biological Interest). 

Author: Franziska Rau (KNIME)

Authors: Daniele Tonini, Maarit Widmann, Corey Weisinger

Considering the plethora of articles, applications, web tutorials and challenges on the data science subject that we’re seeing in the last 3-5 years, it can be pretty surprising to find only a few of them dedicated to time series analysis and forecasting. We’re living in the golden era of data analytics, with plenty of data and algorithms of any kind... but topics like deep learning, artificial intelligence and NLP are attracting basically all of the attention of the practitioners, while the concept of Time Series forecasting is often neglected.

Here are five different methods of sharing your data analysis with key stakeholders

Author: Paolo Tamagnini (KNIME). As first published in DevPro Journal.

There are many different scenarios when building a data science workflow. No matter how complex the data analysis, every data scientist needs to deal with an important final step: communicating their findings to the different stakeholders — decision-makers, managers, or clients. This final step is vital because if the findings cannot be understood, trusted or valued, then the entire analysis will be discarded and forgotten.

Takeaway: learn how to grant access and connect to Google BigQuery, as well as upload data back to Google BigQuer

Authors: Kathrin Melcher, Rosaria Silipo (KNIME). As first published in Dataversity.

Decision trees represent a set of very popular supervised classification algorithms. They are very popular for a few reasons: They perform quite well on classification problems, the decisional path is relatively easy to interpret, and the algorithm to build (train) them is fast and simple.

Authors: Iris Adä and Phil Winters (KNIME)

Want a workflow that uses available in-DB capabilities and moves to a production Spark setup? At the same time it should use special Google services before comparing a KNIME Random Forest to an H2O Random Forest and then automatically choose the correct model to create data that are automatically added to your favourite CRM - so that the new score is placed back into the CRM? No problem in KNIME.

Authors: Maarit Widmann and Rosaria Silipo (KNIME). As first published in The New Stack.

The full big data explosion has convinced us that more is better. While it is of course true that a large amount of training data helps the machine learning model to learn more rules and better generalize to new data, it is also true that an indiscriminate addition of low-quality data and input features might introduce too much noise and, at the same time, considerably slow down the training algorithm.