Run Predictions

import os, json, requests # Define your test dataset path test_path = "test.csv" # Get your API key from the environment api_key = os.getenv("PRIORLABS_API_KEY") headers = {"Authorization": f"Bearer {api_key}"} # Create prediction payload payload = { "task": "classification", "model_id": model_id, # Use model_id from your /v1/fit call } files = { "data": (None, json.dumps(payload), "application/json"), "file": (test_path, open(test_path, "rb")), } predict_response = requests.post( "https://api.priorlabs.ai/v1/predict", headers=headers, files=files, ) print("✅ Predictions:") print(json.dumps(predict_response.json(), indent=2))

{ "duration_seconds": 15, "prediction": [ [ 0.1, 0.9 ], [ 0.8, 0.2 ] ], "task": "classification", "params": { "average_before_softmax": false, "categorical_features_indices": null, "device": [ "cpu" ], "differentiable_input": false, "fit_mode": "fit_preprocessors", "ignore_pretraining_limits": true, "inference_config": null, "inference_precision": "auto", "memory_saving_mode": true, "model_path": "auto", "n_estimators": 8, "n_jobs": null, "n_preprocessing_jobs": 4, "random_state": 42, "softmax_temperature": 0.2 }, "used_credits": 10, "remaining_quota": 90 }

Authorizations

Authorization

string

header

required

Bearer token for authentication, obtained after signing up and generating an API key.

Body

multipart/form-data

data

string

required

A JSON string defining the prediction request parameters.

Required fields:

model_id (str) - Model ID from your previous /v1/fit call
task (str) - Task type: "classification" or "regression"

Optional Config Parameters:

systems (list[str]) - default: ["preprocessing", "text"]. The following preprocessing systems are supported:
- ["preprocessing"] - Applies skrub preprocessing,
- ["text"] - Adds text embeddings for text columns.
n_estimators (int) - Number of estimators in the ensemble (1-10)
model_path (str) - Model checkpoint path from HuggingFace
categorical_features_indices (List[int]) - Indices of categorical features
softmax_temperature (float) - Temperature for softmax scaling
average_before_softmax (bool) - Average before applying softmax
ignore_pretraining_limits (bool) - Ignore pretraining limits
inference_precision (str) - Inference precision ("float32", "float16", "auto")
random_state (int) - Random seed for reproducibility
balance_probabilities (bool) - Balance class probabilities

Optional Params (output configuration):

output_type (str) - Determines prediction output format
- Classification: "probas" (default, probabilities) or "preds" (predictions)
- Regression: "mean" (default, mean value) or "full" (includes quantiles, ei, pi)

file

required

CSV file containing the dataset to predict on.

Response

Prediction completed successfully — returns predicted values or probabilities depending on the task.

duration_seconds

integer

required

Time taken (in seconds) to complete the prediction.

prediction

required

The prediction output. Format depends on task and output_type:

Regression: List of floats (e.g., [1250.5, 3200.8, 980.2])
Classification with output_type: probas: List of lists (probabilities) (e.g., [[0.1, 0.9], [0.8, 0.2]])
Classification with output_type: preds: List of classes (e.g., ["class_0", "class_1"])

task

enum<string>

required

Specifies the type of task to perform — either classification or regression.

Available options:

classification,

regression

used_credits

integer

required

The number of credits consumed by this API call.

remaining_quota

integer

required

Your remaining credit balance after this request.

params

object

The inference parameters that were used during prediction.

Show child attributes

Getting Started

Endpoints

Security

Authorizations

Body

Response