This component generates example data for classification tasks based on the make_classification() function in the Python scikit-learn library. The predictor features are randomly drawn from the standard normal distribution. If desired, some of the features can be redundant or duplicated. The samples are assigned into one or more clusters within each class. Furthermore, it is possible to regulate the class separation within the feature space. For more information see the sklearn documentation: scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html Note: This component requires a Python environment. In this blog post we explain how to setup the KNIME Python extension: knime.com/blog/setting-up-the-knime-python-extension-revisited-for-python-30-and-20