tabh2o-predict

TabH2O - Tabular Prediction API

Keywords: tabular data, classification, regression, timeseries, forecasting, clustering, imputation, prediction, structured data, machine learning, foundation model, zero-shot, no-code ML, API, time_column

What it does

TabH2O is a foundation model for tabular data. Supports four task types:

Task	Description	Requires target?
classification	Predict categorical labels	Yes
regression	Predict continuous values (add time_column for timeseries)	Yes
clustering	Group similar rows (paid plans only)	No
imputation	Fill missing values (paid plans only)	No

Input via JSON arrays or a single CSV file.

API

Endpoint: POST https://tabh2o.h2oai.com/api/v1/predict

Auth: Authorization: Bearer <API_KEY>

Accepts two content types:

• application/json — inline data as arrays
• multipart/form-data — CSV file upload

Option 1: JSON request body

Content-Type: application/json

{
  "train": {
    "data": [[val, val, ...], ...],
    "columns": ["col1", "col2", ..., "target_col"]
  },
  "test": {
    "data": [[val, val, ...], ...],
    "columns": ["col1", "col2", ...]
  },
  "target_column": "target_col",
  "task": "classification",
  "time_column": "date_col",
  "n_clusters": 5,
  "cluster_method": "kmeans"
}

Fields:

Field	Type	Required	Description
train.data	array of arrays	Yes	Training rows
train.columns	array of strings	Yes	Column names for training data
test.data	array of arrays	Yes	Test rows
test.columns	array of strings	Yes	Column names for test data
task	string	Yes	One of: classification, regression, clustering, imputation
target_column	string	Supervised tasks	Name of the target column in train.columns
time_column	string	Optional	Name of the time/date column. Enables timeseries forecasting (regression only). Any format pd.to_datetime() can parse.
n_clusters	integer (2-1000)	Clustering (kmeans)	Number of clusters. Required for kmeans, ignored for dbscan.
cluster_method	string	Optional	Clustering algorithm: kmeans (default) or dbscan.

Rules:

• For supervised tasks (classification, regression): train.data rows include the target value, test.data rows do not. train.columns includes the target, test.columns does not.
• For unsupervised tasks (clustering, imputation): train.columns and test.columns must match. No target column needed.
• For imputation: test data should contain null/missing values to be filled.
• Column order in columns must match the order of values in each data row.
• Cell values can be: string, number, boolean, or null.

Option 2: CSV file upload

Content-Type: multipart/form-data

Field	Type	Required	Description
file	file	Yes	CSV file with header row
task	string	Yes	Task type (see above)
target_column	string	Supervised tasks	Name of the target column
time_column	string	Optional	Name of the time/date column (enables timeseries forecasting, regression only)
n_clusters	integer	Clustering (kmeans)	Number of clusters. Required for kmeans.
cluster_method	string	Optional	kmeans (default) or dbscan.

For supervised tasks: rows where the target column is empty are test rows; the rest are training rows. For unsupervised tasks: all rows are used. For imputation, cells with missing values will be filled.

Responses

Classification (200):

{
  "predictions": ["Yes", "No"],
  "probabilities": [[0.82, 0.18], [0.35, 0.65]],
  "metadata": { "task": "classification", "train_rows": 100, "test_rows": 2, "columns": 5, "time_ms": 245 }
}

Regression (200):

{
  "predictions": [52000.0, 75000.0],
  "confidence_intervals": [[45000, 59000], [68000, 82000]],
  "metadata": { "task": "regression", "train_rows": 100, "test_rows": 2, "columns": 5, "time_ms": 312 }
}

Regression with time_column (200):

{
  "predictions": [161.2, 148.7],
  "confidence_intervals": [[142.0, 180.4], [128.5, 168.9]],
  "metadata": { "task": "regression", "train_rows": 50, "test_rows": 2, "columns": 3, "time_ms": 380 }
}

Clustering (200):

{
  "predictions": [0, 1, 0, 2, 1],
  "metadata": { "task": "clustering", "algorithm": "kmeans", "train_rows": 100, "test_rows": 5, "columns": 3, "n_clusters": 3, "time_ms": 520 }
}

Imputation (200):

{
  "imputed_data": [[25, 50000, 8], [30, 52000, 7]],
  "imputed_columns": ["age", "income", "score"],
  "imputed_mask": [[false, false, false], [false, true, false]],
  "metadata": { "task": "imputation", "train_rows": 50, "test_rows": 2, "columns": 3, "columns_imputed": 1, "time_ms": 890 }
}

Only task-relevant fields are returned. probabilities for classification, confidence_intervals for regression, imputed_data/imputed_columns/imputed_mask for imputation.

Error responses

Status	Error code	Meaning
401	invalid_api_key	Missing, invalid, or revoked API key
422	validation_error	Bad request body, data too large, wrong format
429	rate_limit_exceeded	Too many requests per minute — wait and retry
429	quota_exceeded	Daily or monthly quota reached
503	service_unavailable	Backend temporarily down — retry with backoff
504	timeout	Inference timed out — try a smaller dataset

Limits (free tier)

Limit	Value
Requests/minute	2
Requests/day	20
Requests/month	500
Available tasks	Classification, Regression
Max rows per request	100,000
Max columns	100
Max payload	50 MB

Note: Clustering and imputation are not available on the free tier. Contact sales for access to unsupervised tasks.

Privacy & anonymization

Data is never stored — processed in memory and discarded after the response. Nothing is logged, cached, or used for training.

You can fully anonymize data with zero impact on prediction quality:

• Column names are arbitrary. Rename to c1, c2, etc.
• Categorical values can be mapped to integers. The model learns from patterns, not label semantics.
• Numeric values need no changes.

Usage guidelines for agents

1. Choose the right task type. Classification for categories, regression for continuous numbers (add time_column for forecasting with dates), clustering to discover groups, imputation to fill missing values.
2. Include enough training data. More rows = better predictions. Aim for 20+ training rows for supervised tasks.
3. Keep columns consistent. Train and test must have the same feature columns.
4. Handle errors gracefully. On 429, back off and retry. On 503/504, retry with exponential backoff. On 422, fix the request.
5. Anonymize sensitive data. Rename columns, map categories to integers.
6. Interpret results in context. Use probabilities for classification confidence, confidence_intervals for regression uncertainty, imputed_mask to see what was filled in.

Example: Classification (JSON)

curl -s -X POST https://tabh2o.h2oai.com/api/v1/predict \
  -H "Authorization: Bearer $TABH2O_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "train": {
      "data": [[25,50000,1,"Yes"],[30,60000,3,"No"],[22,45000,0,"Yes"],[35,70000,8,"No"],[28,55000,2,"Yes"],[40,80000,10,"No"]],
      "columns": ["age","income","experience","purchased"]
    },
    "test": {
      "data": [[27,52000,2],[38,75000,7]],
      "columns": ["age","income","experience"]
    },
    "target_column": "purchased",
    "task": "classification"
  }'

Example: Regression (JSON)

curl -s -X POST https://tabh2o.h2oai.com/api/v1/predict \
  -H "Authorization: Bearer $TABH2O_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "train": {
      "data": [[1200,2,1,250000],[1800,3,0,350000],[2400,4,1,500000],[900,1,0,180000],[1500,3,1,310000]],
      "columns": ["sqft","bedrooms","garage","price"]
    },
    "test": {
      "data": [[1600,3,1],[2000,4,0]],
      "columns": ["sqft","bedrooms","garage"]
    },
    "target_column": "price",
    "task": "regression"
  }'

Example: Timeseries (CSV)

date,store,sales
2025-01-01,A,120
2025-01-02,A,135
2025-01-03,A,98
2025-01-04,A,142
2025-01-05,A,155
2025-01-06,A,
2025-01-07,A,

curl -s -X POST https://tabh2o.h2oai.com/api/v1/predict \
  -H "Authorization: Bearer $TABH2O_API_KEY" \
  -F [email protected] \
  -F target_column=sales \
  -F time_column=date \
  -F task=regression

Example: Clustering (CSV)

age,income,spending_score
19,15000,39
21,15000,81
20,16000,6
23,16000,77
31,17000,40

curl -s -X POST https://tabh2o.h2oai.com/api/v1/predict \
  -H "Authorization: Bearer $TABH2O_API_KEY" \
  -F [email protected] \
  -F task=clustering \
  -F n_clusters=3

Example: Imputation (CSV)

age,income,satisfaction,city
25,50000,8,NYC
30,,7,LA
,45000,9,NYC
28,55000,,LA

curl -s -X POST https://tabh2o.h2oai.com/api/v1/predict \
  -H "Authorization: Bearer $TABH2O_API_KEY" \
  -F [email protected] \
  -F task=imputation

Example: Python (file upload)

import requests

response = requests.post(
    "https://tabh2o.h2oai.com/api/v1/predict",
    headers={"Authorization": f"Bearer {API_KEY}"},
    files={"file": ("data.csv", open("data.csv", "rb"))},
    data={
        "target_column": "purchased",
        "task": "classification",
    },
)
print(response.json())