How is data split into training / test sets in rapidminer GO?
I am using rapid miner go and would like to know how data is being split into training / test sets and if its the same for each method (deep learning, gradient boosted trees etc.).
Is there anywhere in the docs that say first 70% of rows are used for training or something like this?
Thank you
Is there anywhere in the docs that say first 70% of rows are used for training or something like this?
Thank you
Find more posts tagged with
Sort by:
1 - 3 of
31
- White paper: https://academy.rapidminer.com/learn/article/how-to-correctly-validate-machine-learning-models
- Videos: https://academy.rapidminer.com/learn/video/validation-demo, https://academy.rapidminer.com/learn/video/validating-a-model
New Altair Community Member
OPThank you,
Is there anymore information on this? I am new to data science and self teaching, so I'm a bit confused by the terminology.
I am asking because I am noticing a difference in the predictive power of my models based on which order the data set they were built on was originally uploaded.
To clarify, the data is 60 / 40 split but what goes into the 60 and 40 respectively is done randomly but ensuring the same distribution is kept?
Or is it the first 60% of rows and last 40% of rows for the split?
Is there anymore information on this? I am new to data science and self teaching, so I'm a bit confused by the terminology.
I am asking because I am noticing a difference in the predictive power of my models based on which order the data set they were built on was originally uploaded.
To clarify, the data is 60 / 40 split but what goes into the 60 and 40 respectively is done randomly but ensuring the same distribution is kept?
Or is it the first 60% of rows and last 40% of rows for the split?
New Altair Community Member
You can find free learning materials on https://academy.rapidminer.com/. I would recommend checking it out.
For example, on validation I found the following materials, that could help you:
When splitting data, Go always shuffles the dataset. In case of a nominal (categorical) label, Go ensures the same distribution.
Regards,
Andras
For example, on validation I found the following materials, that could help you:
When splitting data, Go always shuffles the dataset. In case of a nominal (categorical) label, Go ensures the same distribution.
Regards,
Andras
We use a 60/40 split for every model. If the target column is nominal, Go builds random subsets and ensures that value distribution is the same as in the original dataset. Otherwise, Go builds subsets randomly.
Regards,
Andras