Teaching you how to present your data to drive action
Dummy variables are needed when you want to perform a regression but have categorical data. The easy way to create those dummy variables is with a technique called one hot encoding. KNIME performs one hot encoding with the one to many node. What is a dummy variable? Dummy variables are how we convertRead more about KNIME tutorial: Dummy variables with one hot encoding[…]
The KNIME GroupBy node is how the platform handles grouping. Grouping data is a powerful technique that I use often in the data exploration phase of machine learning and modeling. Almost every data set has variables that you can think of as categories. Grouping based on those categories and then seeing what happensRead more about KNIME tutorial: GroupBy node to group your data[…]
KNIME is an excellent platform I wanted to learn I started filming tutorials on how to do things in KNIME because it was something that I wanted to learn how to use. I’ve use several other platforms for work and school over the years, and was interested in learning KNIME, so you knowRead more about My thoughts after one month of using KNIME[…]
Feature Engineering Feature engineering is an important step to taking models from just OK to great. If you have not heard the term feature, we essentially mean the data that goes into the model. You can think of it the same way you would think of an independent variable in statistics. What isRead more about KNIME tutorial: Feature engineering to improve Kaggle Titanic random forest performance (part 3)[…]
Random Forest Models The random forest model is easy to execute in KNIME. It is a popular model because it is easy to implement, adaptable, and robust to overfitting. Random forests are a common way for new people to get started with machine learning. How do they work? The random forest worksRead more about KNIME tutorial: Random forest machine learning model to predict Kaggle Titanic (part 2)[…]
KNIME Machine Learning Tutorial I love to help people who are climbing the career ladder, looking to make a switch, or established in their fields, to learn to use more analytics and data science in their work. One of the things that has fascinated me for years is how people say they want toRead more about KNIME tutorial: Kaggle Titanic machine learning problem data prep and cleaning (part 1)[…]
To go alongside the other methods I mentioned in the PowerPoint for data science tutorials, I present you with the three commandments of slide writing. These are some of the rare times you will hear me use the words “always” and “never” because I think these warrant it. Keep these in mind asRead more about PowerPoint tutorial for data science (part 4): The three commandments of slide writing[…]
Why do we need joins? The ability to join data tables together is a foundation of any analytics platform, KNIME included. You need to have some way to not only join two or more tables together, but to ensure the records are matched based on some key, perhaps on a specific item or customer. ThisRead more about KNIME tutorial: Performing joins in KNIME[…]
There are four types of joins you can do in KNIME; they are the inner join, left outer join, right outer join and full outer join. I’ll go over what they mean and follow up with an example for each. There are some places that claim there are over twenty types of joins. InRead more about KNIME: the four types of joins explained[…]
The ability to join data tables together is a foundation of any analytics platform, KNIME included. You need to have some way to not only join two or more tables together, but to ensure the records are matched based on some key, perhaps on a specific item or customer. This is where KNIME can comeRead more about KNIME: Joins are the key to using more than one data set[…]