Proteomics_LFQ
Combines peptide identifications from X!Tandem and Comet. Uses normalized feature intensities for peptide quantification. No smart protein inference, all […]
AWSNodes
Amazon Comprehend Entity Tagger: Named-entity tagger based on AWS Comprehend (https://aws.amazon.com/comprehend/features/). Entities that can be detected: […]
Feature Importance based on RF
Feature Importance based on RF This Workflow demonstrates the usage of the Component "RF Feature Importance". It can be used in binary classification […]
ML
J48: Correctly Classified Instances 9614 72.8921 %. Incorrectly Classified Instances 3588 27.1079 %
CS-116 Cola Wars 1985 New Coke v05
[Case Studies] CS-116 Cola Wars 1985 New Coke Explores the cautionary tale against tampering with a well-established and successful brand. This Market […]
Titanic_183_Phase_5_Evaluation_NB
Titanic: Phase 5 (Evaluation) Feature Selection Naive Bayes v7 v8 URL: Data Science Training - Kapitel 18 https://data-science.training/kapitel-18/
Titanic_196_Phase_5_Evaluation_TE
Titanic: Phase 5 (Evaluation) Hyperparameteroptimierung Tree Ensemble v9 URL: Data Science Training - Kapitel 19 https://data-science.training/kapitel-19/
Linear Regression Plotter
<p>Select which feature to plot on the x axis.</p>