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The top-level preprocessing section specifies dataset splitting (train, validation, test), and dataset balancing.

        type: random
        probabilities: [0.7, 0.2, 0.1]

Dataset Splitting

Data splitting is an important aspect of machine learning to train and evaluate machine learning models.

Ludwig supports splitting data into train, validation, and test sets, and this is configured using the top-level preprocessing section of the Ludwig config.

There is no set guideline or metric for how the data should be split; it may depend on the size of your data or the type of problem.

There are a few different methods that Ludwig uses to split data. Each of these methods is specified under the split subsection.

The following splitting methods are currently supported by Ludwig:

Random Split

By default, Ludwig will randomly split the data into train, validation, and test sets according to split probabilities, which by default are: [0.7, 0.1, 0.2].

However, you can specify different splitting probabilities if you'd like. For instance, if you want your dataset to be split according to a 60% train, 15% validation, and 25% test regime, you would use this config:

        type: random
        probabilities: [0.6, 0.15, 0.25]

Fixed Split

For users with pre-defined split that you want to use across experiments, Ludwig supports fixed dataset splitting.

Provide an additional column in your data called split with the following values for each split you want to include in your training/validation/test subset.

  • 0: train
  • 1: validation
  • 2: test

!!! note: Your dataset must contain a train split while the validation and test splits are encouraged, but technically optional.

The following config is an example that would perform fixed splitting using a column named split:

        type: fixed
        column: split

Stratified Split

Sometimes you may want to split your data according to a particular column's distribution to maintain the same representation of this distribution across all your data subsets.

In order to perform stratified splitting, you specify the name of the column you want to perform stratified splitting on and the split probabilities. For example:

        type: stratify
        column: color
        probabilities: [0.7, 0.1, 0.2]

This helps ensure that the distribution of the values of the color feature are roughly the same across data subsets.


This split method is only supported with a local Pandas backend. We are actively working on including support for other data sources like Dask.

Datetime Split

Another common use case is splitting a column according to a datetime column where you may want to have the data split in a temporal order.

This is useful for situations like backtesting where a user wants to make sure that a model trained on historical data would have performed well on unseen future data.

If we were to use a uniformly random split strategy in these cases, then the model may not generalize well if the data distribution is subject to change over time. Splitting the training from the test data along the time dimension is one way to avoid this false sense of confidence, by showing how well the model should do on unseen data from the future.

For datetime-based splitting, we order the data by date (ascending) and then split according to the split_probabilties. For example, if split_probabilities: [0.7, 0.1, 0.2], then the earliest 70% of the data will be used for training, the middle 10% used for validation, and the last 20% used for testing.

The following config shows how to specify this type of splitting using a datetime column named created_ts:

        type: datetime
        column: created_ts
        probabilities: [0.7, 0.1, 0.2]

Data Balancing

Users working with imbalanced datasets can specify an oversampling or undersampling parameter which will balance the data during preprocessing.


In this example, Ludwig will oversample the minority class to achieve a 50% representation in the overall dataset.

    oversample_minority: 0.5


In this example, Ludwig will undersample the majority class to achieve a 70% representation in the overall dataset.

    undersample_majority: 0.7


Dataset balancing is only supported for binary output features currently. We are working to add category support in a future release.


Specifying both oversampling and undersampling parameters simultaneously is not supported.

Sample Ratio

Sometimes users may want to train on a sample of their input training data (maybe there's too much, and we only need 20%). In order to achieve this, a user can specify a sample_ratio to specify the ratio of the dataset to use for training.

By default, the sample ratio is 1.0, so if not specified, all the data will be used for training. For example, if you only want to use 30% of my input data, you could specify a config like this:

    sample_ratio: 0.3

Feature-specific preprocessing

To configure feature-specific preprocessing, please check datatype-specific documentation.