Hello, I am to Machine Learning but I want to explore the possible applications. I have completed L1-DS which also includes a few ML flows. What I was wondering about: in that course you teach a decision tree to predict a certain outcome based on a dataset in which the outcome has already been given. That makes sense to me, otherwise you can’t train the model. However, I understand that the added value of ML is that it allows you to map unclassified data. I would like to learn that step. Is that included somewhere in another course or are there additional pages explaining it? In short, I am quite new to this topic and I am wondering: how do you train a model and then let it work with a new dataset? Hopefully you can point me in the right direction!

@StefanPlanning if you want to explore the new data you would have to provide the truth for them in order to see how a model would perform. Typically you would set aside a portion of the training data in order to see if the model can make sense for your existing data already. There are other techniques to make a model more stable. When you have data stretching a long time back and you expect a very robust scenario you can use data from one period (Month, year) as training and another month or year as Test to get a stability. I would recommend taking more courses and reading about the basics of machine learning.

Machine Learning: continuation

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mlauber71 August 24, 2023, 1:01pm 2

@StefanPlanning a good basic example of a Decision Tree with some description you will find in the Beginners Space of the KNIME Hub:

To score new data you will have to provide the same data with all the columns as before but without the (unknown) target. So for example a collection of customers where you know their ‘features’ but not if they want to buy something.

If you want to learn more about machine learning and KNIME I have collected these hints and links:

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