13_Feature_Selection_exercise
Introduction to Machine Learning Algorithms course - Session 4 Exercise 5 - Combine previously splitted train and test set - Search for the most important […]
13_Feature_Selection_solution
Introduction to Machine Learning Algorithms course - Session 4 Solution to exercise 5 - Combine previously splitted training and test set - Search for the […]
Fragment_generator_synCor_Ubuntu_finalV1
Fragment_generator_synCor_Ubuntu_finalV1 The workflow can be used to generate novel fragments for FBDD using the "syntax corrected" dual encoder model […]
Titanic_182_Phase_4_Modeling_FS_NB_v8
Titanic: Phase 4 (Modeling) Feature Selection Naive Bayes v8 URL: Data Science Training - Kapitel 18 https://data-science.training/kapitel-18/
CRISP-DM
<h1>Data Understanding</h1><h2>Data Import</h2><h2>Data Exploration</h2><h2>Data Visualization</h2><h2></h2>
Synthetic Data Generator (Classification)
This component generates example data for classification tasks based on the make_classification() function in the Python scikit-learn library. The […]
Synthetic Data Generator (Classification)
This component generates example data for classification tasks based on the make_classification() function in the Python scikit-learn library. The […]
Bank Loan Modeling with Auto Categorical Features Embedding
Bank Loan Modeling with Auto Categorical Features Embedding This workflow demonstrates use of Auto Categorical Features Embedding node. It is a Bank Loan […]
06_Basic_Examples_for_Using_the_GroupBy_Node
This workflow shows the many aggregation options that the GroupBy node offers. We start from customer data, group on Gender or more features, and run a few […]