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Hollerith predicts missing values in tables. You hand it a labeled table, it reads that table at prediction time. There is no training step and nothing to tune.
1

Install the SDK

Hollerith is a private SDK. It is not on PyPI, and pip install hollerith will not work. During the closed beta it ships as a pre-built wheel served from your control plane. Your console’s Quickstart tab shows the exact current URL.
Set your API key. Create one on the API Keys page in the console — see Authentication.
Both are required. HOLLERITH_BASE_URL has no default — without it the client raises a ValueError before it ever reaches the API.Confirm the install:
2

Make your first prediction

This example uses the iris sample from the console: 150 rows, 4 columns, one labeled species column.
fit uploads the table and returns a context to predict against. predict reads it.Download the CSV from the console’s Quickstart tab, or run the same dataset there without writing any code.A console run uses your signed-in session, never an API key — the browser is never sent one. It takes the same validation, worker, metering and cleanup path as an SDK job, so it shows up under Usage and bills the same rows.
3

Measure the accuracy

A prediction is worth little without a number beside it. Pass evaluate=True to fit and Hollerith scores itself on held-out rows.
One metric comes back: accuracy for classification, RMSE for regression. Compare it against whatever you are running today — Evaluating accuracy covers how to make that comparison fair.evaluate=True runs a second job and bills it separately.A score means nothing without a floor to compare it against. On a table where 90% of rows share one label, 0.90 is what guessing gets you — Evaluating accuracy covers how to set that floor before you read the number.

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What just happened

fit did not train anything. It sent your table to a context the model reads while predicting, closer to handing someone a reference sheet than making them study for the exam. That is why it takes seconds and why there is nothing to tune. The context is reusable. Predict against it as many times as you need without fitting again.
4

Swap in your own table

Two lines change:
Your table needs one row per thing you are predicting and one column holding the label. Categoricals and missing values are read directly, with no encoding and no imputation.Text and date columns are accepted, but read as categories — the model sees which rows share a value, not what the value means. The columns you predict on must match the columns you fit on.

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Predict a number

Same shape, numeric target. Hollerith reads the task from the target column.
A target of small whole numbers is read as classification unless you say otherwise. Pass task="regression" when that is wrong.

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Next steps