Solutions to "Just KNIME It!" Challenge 23

[sagar_ap]

This is my submission for the Churn prediction challenge.

I added an ROC curve, as done by @MarioNasser before. I wrote my cross validated model to a file and used it on the test data to compare the non cross validated ROC vs the cross validated one. I got a marginally better result.
Notes:
The simple Regression Tree did not work and I don’t understand why.
I would have liked to see the scorer results from multiple scorers together but I don’t know how.

[ersy]

Hi everyone,
Here is my solution.
Accuracy:

• 93,3% with pruning
• 91,2% without pruning

justKnimeit-23 – ersy

[MEPivnenko]

My best result is 94.003% challenge accepted!

KnimeIT_23 – mepivnenko

[arddashti]

Hello KNIMErs, Here is my solution for Challenge 23

justknimeit-23 – arddashti

[AnilKS]

My submission for Challenge 23

Just Knime it-23 – anilks

[martinmunch]

hi

here’s my solution.
I did two versions of the challenge, 1) simple prediction model according to the challenge and 2) an advanced using AutoML-node to benchmark several models and choosing the one with highest accuracy. VERY COOL.

summary:
Decision tree: 93% accuracy
AutoML (Gradient Boosted Trees): 95%

KNIME_challenge23_solution – martinmunch

/cheers

[lelloba]

Hello,

here is my solution for this challenge:

justknimeit - 23 - Raffaello Barri – lelloba

Have a nice weekend,
RB

[kwatari]

Hi, here is my solution.

Challenge 23 Modeling Churn Predictions - Part 1 – kwatari

[siry]

justknimeit-23 – siry

[rfeigel]

Here’s my solution and parameter settings and accuracy statistics.

REF Challenge 23.knwf (453.3 KB)

[cf_123]

Here my solution: jKi-23 – KNIME Hub

[jefleisc]

knime://My-KNIME-Hub/Users/jefleisc/Public/jefleisc-knime_challenge-23

[Victor_G]

Hello dear KNIME users !

Since this topic is about classification with a strong hint about the use of tree methods, I decided to compare three tree-based approaches (I didn’t use XGBoost since some users already tested it):

• Decision Tree,
• Gradient Boosted Tree
• Random Forest .

I tried to put the emphasis on model understanding/explainability, that’s why I added a decision tree view for the Decision Tree and the component “Global Feature Importance” for the Random Forest. I haven’t seen any view for Gradient Boosted Tree, so I’m open for any suggestions

The goal for the team might be to have good churn prediction performance, but also an understanding or insights about the model. For example in the random forest, “State” is the first important feature. That may lead the team to better know their customers and learn how to adapt their strategy in different states…

Here is my workflow : KNIME_Challenge-23 – KNIME Hub

[alinebessa]

Wow! Thanks for going the extra mile! Very cool.

[eamendola]

Hi guys !
This is my take on this. I choose another model in order to show a quick versus between them. Hope you find this useful.

Challenge 23 - Modeling Churn Predictions I – eamendola

Have a nice sunday !

[eamendola]

Great build Martin

[alinebessa]

I looove the dashboard comparing the solutions! Excellent idea!

[alinebessa]

Hello hello! Did you know that you can change types from numbers to strings within the CSV Reader node? It’s in the tab “Transformation”, right by tab “Settings” (see image). You may find it useful in the future in case you want to use fewer nodes!

[alinebessa]

Hi folks,

As always on Tuesdays (but this time earlier than the usual!), here’s our solution to challenge 23. We kept it simple, really sticking to a decision tree model for didactic reasons. We’ll be discussing model selection and parameter optimization later on in this series – but if you already know or feel the itch to do that, go for it!!

Challenge 23 - Modeling Churn Predictions - Solution – alinebessa

We’re very happy with the variety of solutions here. See you tomorrow for another iteration of this challenge! Let’s keep discussing the best practices and principles related to data classification!