Analytics

Let’s start this section with a question. How many different algorithms do you know to solve a classification problem? There are many! Decision Tree, Random Forest, Deep Learning, Logistic Regression, just to name a few options. How to choose? It is hard to say in advance.

Traditionally, a bunch of models are trained and then compared to choose the best performing one. More recently, a bunch of models are trained and cooperate to give the best prediction.

A bunch of models, as we informally said, is commonly called a bag of models, which can be trained with KNIME native nodes but also with R, Weka, Python, H2O, Java, etc...

As far as model selection goes, again, there are different metrics to compare model performances: accuracy, confusion matrix, Cohen’s Kappa, A/B Test and ROC Curve, just to name some options.

To provide some orientation in this variety, this video proposes a review of analytics techniques available within the KNIME Analytics Platform.

The workflows shown in this video can be found on the EXAMPLES server under 50_Applications/28_Predicting_Departure_Delays/01_Analytics50_Applications/28_Predicting_Departure_Delays/01_Analytics^*.