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Predict a number from table columns. The dataset is a single CSV with a header row: feature columns plus one numeric target column. Because the target is a raw numeric value, the label must declare a policy that keeps raw targets inside your secure environment.

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Folder layout

  • One CSV, UTF-8, comma-separated, with a header row. With the CLI, an Excel workbook, a semicolon CSV with decimal commas, or a Latin-1 CSV works too: the copy converts it, and your file stays as it is.
  • Feature columns are numeric or typed text; the target column holds a continuous number.

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Data CSV

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Ingest with the CLI

Pass the CSV file, or a folder that holds only that CSV. Ingest the training and the test split separately:
--holds open_data marks the dataset as open data. If your data is about patients, pass --holds patient_data; if it was generated, pass --holds synthetic_data. Leave --holds out on a terminal and the CLI asks; in a script, the dataset is marked as patient data. The mark decides which use case types you can create on the dataset. --label-policy bucket is the default; it is shown here to make the policy explicit. The CLI infers a SQL type for every column, so --schema is optional. To set the types yourself, pass --schema 'col:TYPE,col:TYPE' and quote it, because a type such as VARCHAR(64) contains shell characters. --schema replaces the inferred schema: list every feature column you want stored, not just the ones you retype. Including the label column in --schema is fine: the ingestor drops it from the stored schema. The CLI skips a reserved column such as id.

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ingest.yaml

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What the ingestor checks

Plus the checks every ingest runs. There is no label-diversity check: a continuous target has no classes.

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Sample dataset

The template ships a synthetic housing CSV with id, square_feet (FLOAT), bedrooms (INT), age (INT) and the target price — the rows shown above are its first rows. The ingest.yaml above ingests it as is; only csv: changes to wherever you staged the file.

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Model settings for this dataset

When you link a model, the platform checks num_feature_points against the dataset (see model parameters by task). It must equal the number of stored feature columns: every column except the target and reserved columns such as id. For the sample, that is 3. The model-zoo templates default to 10, 17 or 50, so edit it. output_classes is not checked for regression. Feature columns reach the model sorted by column name, whatever order the CSV or schema uses. Until the next engine release, training and evaluation report R² under accuracy for regression. Read that value as R², not as a share of correct predictions.

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Next steps