Tabular regression

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.

Folder layout

/data/shared/house-prices/
└── houses.csv

One CSV, UTF-8, comma-separated, with a header row.
Feature columns are numeric or typed text; the target column holds a continuous number.

Data CSV

id,square_feet,bedrooms,age,price
1,1668.08,3,15,285.50
2,1701.78,4,12,320.75
3,1697.01,2,8,245.30

Column	Meaning
feature columns	Declared in `schema` with their SQL type and stored.
target column (`price` here)	Named by `label.column`; not declared in `schema`.
`id`	Optional; cannot be declared in `schema` (reserved). Use `data_id: {strategy: column, column: id}` to keep it as the row id.

ingest.yaml

apiVersion: tracebloc.io/v1
kind: IngestConfig
category: tabular_regression
table: house_prices_train
intent: train
csv: /data/shared/house-prices/houses.csv
schema:
  square_feet: FLOAT
  bedrooms: INT
  age: INT
label:
  column: price
  policy: bucket

Field	Required	Meaning
`csv`	yes	Path to the data CSV.
`schema`	yes	Column name to SQL type for the feature columns. Every schema column must exist in the CSV.
`label`	yes, object form	`column` names the target; `policy` is required. `bucket` sends the platform one of 64 stable hash buckets per value instead of the raw number — the stored rows keep the raw target for training. `passthrough` sends raw values; use it only with a compliance sign-off.
`columns`	no	Per-column `unit` (for example `USD`) and `ordinal` facts.
`data_id`, `spec.csv_options`	no	See the contract.

What the ingestor checks

Check	Rejects
Data types	Duplicate header names; a schema column missing from the CSV; any value that does not fit its declared type; unknown SQL types.

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

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.

Next steps

Stage the data and run the ingest: Prepare Data
Shared rules for every template: Dataset templates

Overview

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Tools & Help

Tabular regression

Folder layout

Data CSV

ingest.yaml

What the ingestor checks

Sample dataset

Next steps

​Folder layout

​Data CSV

​ingest.yaml

​What the ingestor checks

​Sample dataset

​Next steps

Folder layout

Data CSV

ingest.yaml

What the ingestor checks

Sample dataset

Next steps