upload_dataset
Get a secure upload URL for your dataset. We recommend this for most workflows: your data is sent directly to cloud storage instead of through the chat, so the agent can handle larger datasets without running into context limits or long execution time.
This tool returns a dataset_id and upload_url - valid for 60 minutes. Call this tool separately for uploading the training set and test dataset.
Required Parameters
String
required
The filename for the dataset. Must be
train.csv for training data or test.csv for test data."train.csv"— Training data"test.csv"— Test data, predictions will be generated for
fit_and_predict_from_dataset
Fit the TabPFN model on your pre-uploaded data and generate predictions. Use this tool when you want to fit a new model from scratch.
Upload both dataset CSV files with upload_dataset first, then pass the two dataset IDs here.
Required Parameters
String
required
The dataset ID you got from
upload_dataset for your training CSV. That file should include all input columns plus the column you want to predict (the target).String
required
The dataset ID you got from
upload_dataset for your test CSV. It should have the same input columns as the training file, but not the target column.String
required
The name of the target column in the training dataset — e.g.
"price" or "churned".Literal
required
The type of the predictive task.
"classification"— Predict a category or class, including probability distributions"regression"— Predict a continuous value
Optional Parameters
String
default:"null"
Prediction output type, default
"preds" for classification and "mean" for regression.Returns
String
Unique ID for the fitted model. Save this value to reuse the model later with predict tools.
String
Pre-signed URL to download the full prediction results as a JSON file. Valid for 60 minutes. Use HTTP GET to retrieve.
Object
Preview of the first predictions (up to 50 samples). Contains a
note describing how many predictions are shown vs total, and a predictions array with the preview data. Download the full results via download_url for all predictions.Object
Summary statistics of the predictions, varying by task type.
- Classification:
num_samples,class_distribution,mean_confidence,min_confidence,max_confidence - Regression:
num_samples,mean,std,min,max,quantile_25,quantile_50,quantile_75
predict_from_dataset
Run predictions with a previously fitted model on a new test set.
Use upload_dataset to upload your new test dataset file, then pass that dataset_id and the model_id you got from a previous fit_and_predict_* call. The test CSV must have the same set of features the model was fitted on.
Required Parameters
String
required
The model ID of the previously fitted model, from
fit_and_predict_from_dataset or fit_and_predict_inline.String
required
The dataset ID of the test dataset predictions will be made from.
Literal
required
The type of the predictive task.
"classification"— Predict a category or class, including probability distributions"regression"— Predict a continuous value
Optional Parameters
String
default:"null"
Prediction output type, default
"preds" for classification and "mean" for regression.Returns
String
Echo of the model ID used for prediction.
String
Pre-signed URL to download the full prediction results as a JSON file. Valid for 60 minutes. Use HTTP GET to retrieve.
Object
Preview of the first predictions (up to 50 samples). Contains a
note describing how many predictions are shown vs total, and a predictions array with the preview data. Download the full results via download_url for all predictions.Object
Summary statistics of the predictions, varying by task type.
- Classification:
num_samples,class_distribution,mean_confidence,min_confidence,max_confidence - Regression:
num_samples,mean,std,min,max,quantile_25,quantile_50,quantile_75
fit_and_predict_inline
Use this tool when you want to fit a new model from scratch. It fits on your data and immediately returns predictions for your test set, along with a model_id for future reuse.
Required Parameters
Matrix
required
Training features as a 2D array where rows represent samples and columns represent features.
- Shape:
(n_train_samples, n_features) - Data types: Numeric (int/float) or categorical (string) values
- Flexibility: Handles missing values, outliers, and mixed data types automatically
Example
Example
Vector
required
Training targets as a 1D array that must align with
X_train rows.- Shape:
(n_train_samples,) - Classification: Class labels (e.g.,
[0, 1, 0]or["cat", "dog", "cat"]) - Regression: Numeric values (e.g.,
[23.5, 45.2, 12.8])
Matrix
required
Test features as a 2D array for generating predictions.
- Shape:
(n_test_samples, n_features) - Critical: Must have the same number of features as
X_train
Example
Example
Literal
required
The type of the predictive task.
"classification"— Predict a category or class, including probability distributions"regression"— Predict a continuous value
Optional Parameters
String
default:"null"
Prediction output type, default
"preds" for classification and "mean" for regression.Returns
String
Unique ID for the fitted model. Save this value to reuse the model later with predict tools.
Array
Prediction results in the format specified by
output_type. For classification with "preds", returns a 1D array of class labels. With "probas", returns a 2D array of class probabilities. For regression with "mean", returns a 1D array of predicted values.Object
Summary statistics of the predictions, varying by task type.
- Classification:
num_samples,class_distribution,mean_confidence,min_confidence,max_confidence - Regression:
num_samples,mean,std,min,max,quantile_25,quantile_50,quantile_75
predict_inline
Generate new predictions using a previously fitted TabPFN model.
Use this tool after calling fit_and_predict_* to make predictions on new data using an existing model.
Required Parameters
String
required
ID of a previously fitted model, returned from
fit_and_predict_*.Example
Example
Matrix
required
Test features as a 2D array for generating predictions.
- Shape:
(n_test_samples, n_features) - Critical: Must have the same number of features the model was originally fitted on
Example
Example
Literal
required
The type of the predictive task.
"classification"— Predict a category or class, including probability distributions"regression"— Predict a continuous value
Optional Parameters
String
default:"null"
Prediction output type, default
"preds" for classification and "mean" for regression.Returns
String
Echo of the model ID used for prediction.
Array
Prediction results in the format specified by
output_type. Same structure as fit_and_predict_inline.Object
Summary statistics of the predictions, varying by task type.
- Classification:
num_samples,class_distribution,mean_confidence,min_confidence,max_confidence - Regression:
num_samples,mean,std,min,max,quantile_25,quantile_50,quantile_75