A brief overview of the Interpretable AI features in #Knime. This video covers the Partial Dependence Plot in Knime. A Partial Dependence Plot shows how a single continuous predictor variable impacts the output of a model when holding all other predictors constant....
To improve Machine Learing Interpretability, Knime has introduced Individual Conditional Expectation (ICE) plots. ICE plots enable you to drill down to the level of individual observations, and show you what would happen to the model’s prediction if you varied one...
Knime has a released a node for making Partial Dependence Plots for Machine Learning models. Partial Dependence Plots help in Interpreting Machine Learning models. Here is a short video intro to the Partial Dependence Plots in Knime.
Knime has introduced new features in the latest release to improve the Interpretability of the Machine Learning models. One such node is the Binary Classification Inspector. This node let’s you visually explore the performance metrics and also provides an easy...
Recently I had the need to find all the Ngrams from large corpus. The NGrams ranged from 2 words to 40 words per ngram. To calculate the longest Ngrams, I had to find Ngrams that are subset of larger Ngram and remove, keeping the longer one. This ending up with the...