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Longitudinal Classification

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Dataset Setup

Start with how your data is already split.

Choose this first. AutoML will turn on only the upload fields and column choices that match that setup.

1. Evaluation setup

At the default setting, AutoML creates separate training, validation, and locked-test portions from this file.

Current setup

One file; let AutoML split it

Default: approximately 60% trains models, 20% checks model choices, and 20% is held back for final reporting.

2. Upload the matching file or files

Upload one dataset. Validation and test file uploads stay inactive for this setup.

Choose Separate train, validation, and/or test files above to open dedicated upload cards for each role.

One-file setup

Dataset File

Required

Required. AutoML reads its columns for mapping, optional split-column choice, and optional sampling weights.

3. Optional columns

These lists populate after the dataset or training file is read.

Sampling Weight Column

Optional. Use when rows represent a weighted sample of the population.

Outcome to predict

Choose the result you want AutoML to predict at each time point.

Person or record ID

Choose the column that keeps repeated records for the same person, item, or case together.

When each record happened

Choose the date, visit number, or time value that puts records in order.

Compare model performance across groups (optional)

Check whether the model performs differently for groups such as sex, age group, region, or study site. This does not change which predictors are used to train the model.

Do you want a group comparison?

No Yes

Check your data

Find problems before training starts.

Check the file for missing information, unsuitable columns, and other issues that may affect the results.

No charge. No training starts.

Step 4

Choose what to compare, then start

Your recommended setup appears after the data check is complete.

Recommended setup

Start with the balanced comparison.

AutoML will compare a practical mix of suitable models. Change it only when you have a specific reason.

Strategy

Balanced comparison

A practical first comparison using different types of models.

Models selected

Preset-driven

All eligible models remain visible below; this run style only sets the starting checkboxes.

Choose a comparison approach

Balanced comparison is selected by default. Choose another approach only when its time and cost trade-off fits your goal.

Before you start

Review the cost and expected outputs

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Expected outputs: Metrics summary, report, and prediction-ready files will appear here once this service loads.

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Longitudinal Classification

Longitudinal Classification

Preparing workflow guidance...

Start with how your data is already split.

Find problems before training starts.

What needs your attention

Core Longitudinal Models

Classical Statistical Models

Choose what to compare, then start

Start with the balanced comparison.

Review the cost and expected outputs

Live job updates