tabh2o

name	tabh2o
description	Makes predictions on tabular data using the TabH2O foundation model API. Supports classification, regression (including timeseries forecasting), clustering, missing value imputation, and anomaly detection, with optional global feature importance for classification and regression. Send data as JSON or CSV, get results back.
version	1.0.0
api_endpoints	["https://tabh2o.h2oai.com/api/v1/predict","https://tabh2o.h2oai.com/api/v1/forecast","https://tabh2o.h2oai.com/api/v1/explore"]
auth	Bearer API key (obtain at https://tabh2o.h2oai.com)

TabH2O - Tabular Prediction API

Keywords: tabular data, classification, regression, timeseries, forecasting, clustering, imputation, anomaly detection, outlier detection, prediction, structured data, machine learning, foundation model, zero-shot, no-code ML, API, time_column, feature importance, feature attribution, explainability, integrated gradients

What it does

TabH2O is a foundation model for tabular data. Supports these task types across three endpoints:

Task	Description	Endpoint	Requires target?
classification	Predict categorical labels	/api/v1/predict	Yes
regression	Predict continuous values	/api/v1/predict	Yes
timeseries forecasting	Predict future values over time (regression + time_column)	/api/v1/forecast	Yes
imputation	Fill missing values (paid plans only)	/api/v1/predict	No
clustering	Group similar rows (paid plans only)	/api/v1/explore	No
anomaly_detection	Score rows by how anomalous they are (paid plans only)	/api/v1/explore	No

Input via JSON arrays or a single CSV file. Classification and regression requests can additionally return global feature importance scores (Integrated Gradients) by setting feature_importance: true.

API

Three endpoints share the same request/response format; pick by task:

• POST https://tabh2o.h2oai.com/api/v1/predict — per-row predictions: classification, regression, imputation. All require a test set; target_column is required for classification/regression (imputation has none).
• POST https://tabh2o.h2oai.com/api/v1/forecast — timeseries forecasting (regression over time). Requires a target_column and a time_column; task may be omitted (it is always regression). time_column is only accepted here.
• POST https://tabh2o.h2oai.com/api/v1/explore — unsupervised discovery (clustering, anomaly_detection). No target column; test is optional (omit it to operate on the training set itself).

Sending a task to the wrong endpoint returns 422 wrong_endpoint telling you which one to use.

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"
}

Task-specific fields (time_column, n_clusters, cluster_method, params) are described in the table below; add them only for the tasks/endpoints that use them.

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	classification/regression	Test rows (for imputation, the rows whose missing cells to fill). Optional for imputation, clustering, and anomaly_detection — omit to operate on the training set itself.
test.columns	array of strings	With test	Column names for test data
task	string	Yes (omit on /forecast)	One of: classification, regression, imputation, clustering, anomaly_detection. On /api/v1/forecast it may be omitted (always regression).
target_column	string	classification/regression	Name of the target column in train.columns. Only valid for classification/regression/forecasting (not imputation, clustering, or anomaly_detection).
time_column	string	Forecast	Name of the time/date column (any format pd.to_datetime() can parse). Enables timeseries forecasting — send the request to /api/v1/forecast, where it is required. Also accepted on /api/v1/predict with task=regression for backwards compatibility (deprecated); rejected on /api/v1/explore.
n_clusters	integer (2-1000)	Clustering (kmeans)	Number of clusters. Required for kmeans, ignored for dbscan. Only valid for clustering.
cluster_method	string	Optional	Clustering algorithm: kmeans (default) or dbscan. Only valid for clustering.
params	object	Optional	Server-side tuning knobs: n_estimators (1-128), batch_size (1-128). Not applicable to anomaly_detection. Sensible defaults if omitted. JSON requests only — not supported for CSV file uploads.
feature_importance	boolean	Optional	Set true to also return global per-feature importance scores (Integrated Gradients) alongside the predictions. classification/regression on /api/v1/predict only (not forecasting or unsupervised tasks); requires target_column; max 100 features.

Rules:

• classification, regression, and imputation go to /api/v1/predict; clustering and anomaly_detection go to /api/v1/explore. Timeseries forecasting goes to /api/v1/forecast. The wrong pairing returns 422 wrong_endpoint.
• Timeseries forecasting (/api/v1/forecast, regression over time): provide time_column (required there); task may be omitted. For backwards compatibility, /api/v1/predict also serves task=regression + time_column as a forecast (deprecated — prefer /api/v1/forecast).
• For classification/regression: train.data rows include the target value, test.data rows do not. train.columns includes the target, test.columns does not.
• For imputation: no target column; test.data rows should contain the null/missing cells to fill (and test.columns must match train.columns). test is optional — omit it to impute the training rows themselves.
• For clustering and anomaly_detection (/explore): train.columns and test.columns must match. No target column. test is optional — omit it to operate on the training set itself.
• For anomaly_detection: the response returns anomaly_scores (higher = more anomalous).
• feature_importance: true adds a feature_importance array to the response (see Responses). Classification and regression only; rejected with 422 for forecasting, unsupervised tasks, and datasets with more than 100 features. The computation runs synchronously in the same request, so allow a generous client timeout.
• 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	classification/regression	Name of the target column
time_column	string	Forecast	Time/date column. Enables timeseries forecasting; send the request to /api/v1/forecast (required there; deprecated compat on /api/v1/predict with task=regression; rejected on /api/v1/explore).
n_clusters	integer	Clustering (kmeans)	Number of clusters. Required for kmeans.
cluster_method	string	Optional	kmeans (default) or dbscan.
feature_importance	string	Optional	true to also return global feature importance (classification/regression only, max 100 features).

Post classification/regression/imputation to /api/v1/predict, timeseries forecasting to /api/v1/forecast, and clustering/anomaly_detection to /api/v1/explore (same as JSON). For classification/regression: rows where the target column is empty are test rows; the rest are training rows. For clustering, imputation, and anomaly detection: all rows are used. For imputation, cells with missing values will be filled. params is JSON-only — it is not supported for CSV file uploads.

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 }
}

Classification or regression with feature_importance: true (200): the usual fields, plus a feature_importance array:

{
  "predictions": ["No", "Yes"],
  "probabilities": [[0.63, 0.37], [0.20, 0.80]],
  "feature_importance": [
    { "feature": "income", "importance": 0.71, "mean_abs_attribution": 0.165 },
    { "feature": "age", "importance": 0.29, "mean_abs_attribution": 0.068 }
  ],
  "metadata": { "task": "classification", "train_rows": 4, "test_rows": 2, "columns": 3, "time_ms": 1210 }
}

Global importance per feature, sorted descending. importance is the normalized share for ranking — shares sum to 1. mean_abs_attribution is the un-normalized mean |attribution| across the explained test rows, in model-output units (class probability for classification, target units for regression) — use it for absolute thresholds or comparisons across requests.

Anomaly detection (200):

{
  "anomaly_scores": [0.12, 0.94, 0.08, 0.71],
  "metadata": { "task": "anomaly_detection", "train_rows": 200, "test_rows": 4, "columns": 6, "time_ms": 410 }
}

Higher anomaly_scores = more anomalous.

Only task-relevant fields are returned. probabilities for classification, confidence_intervals for regression, imputed_data/imputed_columns/imputed_mask for imputation, anomaly_scores for anomaly detection, feature_importance only when requested.

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, imputation, and anomaly detection are not available on the free tier. Contact sales for access to these 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 and endpoint. Classification (categories), regression (continuous numbers), and imputation (fill missing values) go to /api/v1/predict. Timeseries forecasting (regression with a time_column) goes to /api/v1/forecast. Clustering (discover groups) and anomaly_detection (flag outliers) go to /api/v1/explore.
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.
7. Explain predictions when the user asks "why". For classification/regression, set feature_importance: true to get a ranked list of which features drive the model — useful for feature selection, sanity checks, and explaining results. Expect the request to take longer.

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/forecast \
  -H "Authorization: Bearer $TABH2O_API_KEY" \
  -F file=@sales.csv \
  -F target_column=sales \
  -F time_column=date

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/explore \
  -H "Authorization: Bearer $TABH2O_API_KEY" \
  -F file=@customers.csv \
  -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 file=@survey.csv \
  -F task=imputation

Example: Anomaly detection (JSON)

Score each test row by how anomalous it looks relative to the training rows. Omit test to score the training set against itself.

curl -s -X POST https://tabh2o.h2oai.com/api/v1/explore \
  -H "Authorization: Bearer $TABH2O_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "train": {
      "data": [[25,50000,1],[30,60000,3],[22,45000,0],[35,70000,8],[28,55000,2]],
      "columns": ["age","income","experience"]
    },
    "test": {
      "data": [[27,52000,2],[99,5000000,40]],
      "columns": ["age","income","experience"]
    },
    "task": "anomaly_detection"
  }'

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())