JKISeason2-13_rev00
You ara a data scientist working for a real estate company, and heard a rumour that the "average number of rooms per dwelling" (RM) may be connected to the […]
JKISeason2-13
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JKISeason3-18_sryu
<p><strong>Explaining Cancer Predictions</strong></p><p><strong>Challenge 18</strong></p><p><br><strong>Level: </strong>Hard<br><br><strong>Description: […]
01_Explainable_Artificial_Intelligence_(XAI)_Simple
This application is a simple example of AutoML with KNIME Software for binary and multiclass classification. The output models are then explained via the […]
01_Explainable_Artificial_Intelligence_(XAI)_Simple
This application is a simple example of AutoML with KNIME Software for binary and multiclass classification. The output models are then explained via the […]
01_Explainable_Artificial_Intelligence_(XAI)_Simple
eXplainable Artificial Intelligence (XAI) - Simple This application is a simple example of AutoML with KNIME Software for binary and multiclass […]
02_Explainable_Artificial_Intelligence_(XAI)_Complex
eXplainable Artificial Intelligence (XAI) - Complex This application is a simple example of AutoML with KNIME Software for binary and multiclass […]
justknimeit-24
Challenge 24: Modeling Churn Predictions - Part 2. Description: Just like in last week’s challenge, a telecom company wants you to predict which customers […]
Challenge 24 - Churning Problem Part 2 - Solution
Just like in last week’s challenge, a telecom company wants you to predict which customers are going to churn (that is, going to cancel their contracts) […]