Tabular classification

Predict a class from table columns. The dataset is a single CSV with a header row: feature columns plus one class column. There are no per-sample files.

Folder layout

/data/shared/churn/
└── customers.csv

One CSV, UTF-8, comma-separated, with a header row.
Every column has one consistent type. Feature columns are typically numeric; the class column may be text or integer codes.

Data CSV

id,feature_00,feature_01,feature_02,label
1,1.6680773978214032,-0.903902455,6.661496229171176,0
2,0.7017820066004041,1.9789541930566035,5.826137181850707,0
4,4.427524335645055,-1.543700586,6.2987092866052485,1

Column	Meaning
feature columns	Declared in `schema` with their SQL type and stored as table columns. A column that is not in `schema` is not stored.
class column (`label` here)	Named by `label:`; not declared in `schema`. At least two distinct values are required.
`id`	Optional in your CSV, but it cannot be declared in `schema` (`id` is reserved). To use it as the row id, set `data_id: {strategy: column, column: id}` — only if it carries no personal data.

In schema columns, empty cells and exactly the tokens NA, N/A, n/a, NULL, null, None, none, NaN, nan, <NA>, #N/A (case-sensitive) are stored as NULL.

ingest.yaml

apiVersion: tracebloc.io/v1
kind: IngestConfig
category: tabular_classification
table: churn_train
intent: train
csv: /data/shared/churn/customers.csv
schema:
  feature_00: FLOAT
  feature_01: FLOAT
  feature_02: FLOAT
label: label

Field	Required	Meaning
`csv`	yes	Path to the data CSV.
`schema`	yes	Column name to SQL type for every feature column you want stored. Every schema column must exist in the CSV header. See SQL types.
`label`	yes	Name of the class column. String shorthand is fine; the value is sent to the platform as is.
`columns`	no	Per-column `unit` and `ordinal` facts for consistency checks across combined datasets.
`data_id`	no	Row id strategy; see the contract.
`spec.csv_options`	no	`delimiter`, `quotechar`, `escapechar`, `chunk_size`.

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 (non-numeric in `INT`/`FLOAT`, over-long `VARCHAR`, `inf`, out-of-range integers, unparseable dates); unknown SQL type names. The whole file is scanned.
Label diversity	Fewer than two distinct label values (after whitespace stripping).

Plus the checks every ingest runs.

Sample dataset

The template ships a synthetic CSV with id, three FLOAT features (feature_00, feature_01, feature_02) and a binary label column — 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 classification

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