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NEUROBLOCKS STUDIO · Examples & Tutorials

Dataset Import & Train/Validation/Test Split

Import the training data and define validation/test data or automatic split weights.

Learn how to use NeuroBlocks Studio for Dataset Import & Train/Validation/Test Split with real, manually matched screenshots. This tutorial covers setup, execution, result interpretation and practical checks.

What this tutorial covers

Import the training data and define validation/test data or automatic split weights.

Before you start：You need access to the product and should ideally follow the tutorial using your own real parameters or data. The screenshots below were manually rematched to avoid the image/topic mismatches seen in the previous version.
Dataset Import & Train/Validation/Test Split - Open the relevant module and confirm the starting state.
Open the relevant module and confirm the starting state.
Dataset Import & Train/Validation/Test Split - Configure the key inputs that match the topic.
Configure the key inputs that match the topic.
Dataset Import & Train/Validation/Test Split - Run the workflow and inspect the result/status.
Run the workflow and inspect the result/status.

Step-by-step workflow

1Open the module

After the model structure is ready, inspect the Dataset area on the left. Confirm that the visible page really matches the tutorial topic before changing any settings.

2Configure the input

Prepare and check training data, validation/test data and the automatic split weights.

3Run and watch the status

Import the data and confirm the dataset state for each split. Watch the visible status, plot or log area while the workflow is running.

4Inspect the result

Pay special attention to the dataset split state ready for training.

5Verify and iterate

Keep an independent test set when possible instead of repeatedly tuning against it. Change one meaningful parameter and rerun so you can verify whether the output changes in the expected direction.

How to read the result

  • The main output of this workflow is the dataset split state ready for training.
  • Read the result together with the exact inputs used for the run; a screenshot alone rarely tells the whole story.
  • Keep an independent test set when possible instead of repeatedly tuning against it.

Troubleshooting & practical tips

  • If nothing changes, first confirm that the correct module is open.
  • If the output looks unrealistic, check units, field mapping and input scale first.
  • For comparisons, change one key parameter at a time whenever possible.
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