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Knowledge Studio’s novelty and outlier detector node makes it easy to identify anomalies in a dataset and remove them if desired. The software supports three different methods for detecting outliers: Isolation Forest, Local Outlier Factor, and One Class SVM. This video demonstrates how to use the novelty and outlier…
XGB stands for “eXtreme Gradient Boosting” and is often referred to as XGBoost. Knowledge Studio includes an XGB node for predictive modeling that data scientists can use to develop solutions for classification and regression problems. Knowledge Studio’s XGB implementation supports models with several types of dependent…
Knowledge Studio supports analysis of Shapley values, a solution concept from the world of cooperative game theory. Data scientists can use Shapley values to explain individual predictions of black box machine learning models, including random forest and boosting models. This video demonstrates how to use Knowledge…