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 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 ./customers_train.csv --task tabular_classification --name churn_train --intent train --label-column label
tracebloc data ingest ./customers_test.csv  --task tabular_classification --name churn_test  --intent test  --label-column label

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_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 (with the CLI, inferred for you)	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 taken from it. 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 it against the dataset (see model parameters by task):

num_feature_points must equal the number of stored feature columns: every column except the label and reserved columns such as id. For the sample, that is 3.
output_classes must equal the number of distinct labels. For the sample, that is 2.

Most model-zoo templates default to 50 features and 5 classes, so edit both. Feature columns reach the model sorted by column name, whatever order the CSV or schema uses.

Next steps

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

Overview

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Releases

Tabular classification

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