Author: Maarit Widmann

Wheeling like a hamster in the data science cycle? Don’t know when to stop training your model?

Model evaluation is an important part of a data science project and it’s exactly this part that quantifies how good your model is, how much it has improved from the previous version, how much better it is than your colleague’s model, and how much room for improvement there still is.

In this series of blog posts, we review different scoring metrics: for classification, numeric prediction, unbalanced datasets, and other similar more or less challenging model evaluation problems.

Author: Jeany Prinz

One of the great advantages of KNIME Analytics Platform is its ability to analyze diverse types of data. Today we want to move to the outer edge of the alphabet and look into data from the Zika virus. Our analysis is inspired by the Microreact project¹ “Zika virus in the Americas”² and is a nice use case for the exploration of epidemiological data with KNIME Analytics Platform. Epidemiology is the study of the distribution and determinants of health-related states or events³. In this post, therefore, we will answer the question: What routes did the Zika virus take as it spread across the globe, and how did its genetic makeup change the way it did? To this end, we will investigate and visualize both geolocational and phylogenetic data from the Zika virus. Using generic JavaScript nodes, we will create our own dynamic views and wrap them into a composite interactive view.

Even if you deal with very different data on a day-to-day basis, this blog post is still of high value, as we show how to increase the flexibility of your analysis using interactive generic JavaScript views.

Authors: Paolo Tamagnini, Simon Schmid, and Christian Dietz

Implementing a Web-based Blueprint for Semi-automated Machine Learning, using KNIME Analytics Platform

This article is a follow-up to our introductory article on the topic, “How to automate machine learning.” In this second post, we describe in more detail the techniques and algorithms happening behind the scenes during the execution of the web browser application, proposing a blueprint solution for the automation of the machine learning lifecycle.

The price to pay for automated machine learning (aka AutoML) is the loss of control to a black box kind of model. While such a price might be acceptable for circumscribed data science problems on well-defined domains, it might prove a limitation for more complex problems on a wider variety of domains. In these cases, a certain amount of interaction with the end users is actually desirable. This softer approach to machine learning automation — the approach we take at KNIME — is obtained via guided automation, a special instance of guided analytics.

Author: Craig Cullum

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when website texts and Word documents are compared?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: Twitter and Azure. Sentiment Analysis via API.

Author: David Kolb

A lot of people associate the word “development” and KNIME Analytics Platform with creating workflows with nodes. But what exactly is a node and where do nodes come from?

A node is the smallest programming unit in KNIME. Each node serves a dedicated task, from very simple tasks - like changing the name of a column in a table - to very complex tasks - such as training a machine learning model. A node is contained in an extension. One of the jobs a developer does at KNIME is create new extensions or nodes for existing extensions. However, as openness is very important for us at KNIME, everyone can contribute to our platform.

There are lots of ways to extend KNIME, but node development, i.e. writing extensions, to add the specific functionality you or your company needs, is probably the most common.

Authors: Maarit Widmann, Anna Martin, Rosaria Silipo

Do you remember the Iron Chef battles?

It was a televised series of cook-offs in which famous chefs rolled up their sleeves to compete in making the perfect dish. Based on a set theme, this involved using all their experience, creativity, and imagination to transform sometimes questionable ingredients into the ultimate meal.

Authors: Paolo Tamagnini, Simon Schmid, and Christian Dietz

Is it possible to fully automate the data science lifecycle? Is it possible to automatically build a machine learning model from a set of data?

Indeed, in recent months, many tools have appeared that claim to automate all or parts of the data science process. How do they work? Could you build one yourself? If you adopt one of these tools, how much work would be necessary to adapt it to your own problem and your own set of data?

Author: Casiana Rimbu

In March 2019, we hosted the KNIME Spring Summit for the twelfth time. It brought together KNIME users from all over the world. If you weren't able to make it to Berlin this year, watch the live video recordings from the summit, below, to learn about what’s new in KNIME Analytics Platform and KNIME Server.

Author: Rosaria Silipo

LSTM recurrent neural networks can be trained to generate free text.
Let’s see how well AI can imitate the Bard.

“Many a true word hath been spoken in jest.”
― William Shakespeare, King Lear

“O, beware, my lord, of jealousy;
It is the green-ey’d monster, which doth mock
The meat it feeds on.”
― William Shakespeare, Othello

Authors: Rosaria Silipo and Kathrin Melcher

The key to perform any text mining operation, such as topic detection or sentiment analysis, is to transform words into numbers, sequences of words into sequences of numbers. Once we have numbers, we are back in the well-known game of data analytics, where machine learning algorithms can help us with classifying and clustering.

We will focus here exactly on that part of the analysis that transforms words into numbers and texts into number vectors: text encoding.