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 with the CLI

Pass the CSV file, or a folder that holds only that CSV. Ingest the training and the test split separately:

tracebloc data ingest ./houses_train.csv --task tabular_regression --name house_prices_train --intent train --label-column price --label-policy bucket
tracebloc data ingest ./houses_test.csv  --task tabular_regression --name house_prices_test  --intent test  --label-column price --label-policy bucket

--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.

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 (with the CLI, inferred for you)	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.

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.

Next steps

Ingest the data: Prepare Data
Shared rules for every template: Dataset templates

Overview

Create a Use Case

Join a Use Case

Environment Setup

Tools & Help

Releases

Tabular regression

Folder layout

Data CSV

Ingest with the CLI

ingest.yaml

What the ingestor checks

Sample dataset

Model settings for this dataset

Next steps

​Folder layout

​Data CSV

​Ingest with the CLI

​ingest.yaml

​What the ingestor checks

​Sample dataset

​Model settings for this dataset

​Next steps

Folder layout

Data CSV

Ingest with the CLI

ingest.yaml

What the ingestor checks

Sample dataset

Model settings for this dataset

Next steps