IconProteomics_​LFQ 

Combines peptide identifications from X!Tandem and Comet. Uses normalized feature intensities for peptide quantification. No smart protein inference, all […]

IconProteomics_​LFQ 

Combines peptide identifications from X!Tandem and Comet. Uses normalized feature intensities for peptide quantification. No smart protein inference, all […]

IconAWSNodes 

Amazon Comprehend Entity Tagger: Named-entity tagger based on AWS Comprehend (https://aws.amazon.com/comprehend/features/). Entities that can be detected: […]

IconFeature 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 […]

IconML 

J48: Correctly Classified Instances 9614 72.8921 %. Incorrectly Classified Instances 3588 27.1079 %

IconCS-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 […]

IconTitanic_​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/

IconTitanic_​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/