Tabular Predictive Models - Predicts outcomes on structured tabular data using a transformer model that handles mixed types natively.
MCP Server Connections - Ships an MCP server that AI assistants connect to for tabular data predictions.
Class Probability Estimation - Outputs probability scores for each class, enabling confidence-based decisions and log-loss evaluation.
Dataset Class Balancing - Reweights predicted probabilities so each class contributes equally to the evaluation metric.
Synthetic Data Generators - Creates realistic synthetic datasets that preserve the statistical structure of original data for augmentation and privacy-preserving analytics.
Native Mixed-Type Processors - Processes numerical, categorical, and free-text columns natively without any preprocessing or encoding.
Synthetic Dataset Pre-Training - Pre-trains a transformer on millions of synthetic datasets for single-pass predictions.
Datetime Component Extractors - Decomposes raw datetime objects into numeric components like year, month, and day of week for model input.
Tabular Prediction Scaling - Scales tabular predictions to millions of rows with sub-millisecond inference per row.
Native Missing Value Handlers - Handles missing values natively in raw tabular input without requiring any preprocessing or imputation.
Model Fitting via API - Fits a model on uploaded training data via API and returns a model identifier for predictions.
Default Setting Fits - Trains TabPFN on tabular data without explicit preprocessing, producing near-instant fits and probability predictions.
MCP Server Integrations - Provides an MCP server that connects AI assistants to TabPFN for natural language predictions.
Python SDKs - Provides a scikit-learn-compatible Python SDK for cloud-backed model inference.
Prediction Service Deployments - Ships a deployment option that runs the prediction service inside a customer's own cloud environment.
Automatic Mixed-Type Processors - Processes numerical, categorical, and missing values directly without manual preprocessing or encoding.
Prediction API Services - Provides a REST and Python API for fitting models and generating predictions on tabular data.
Model Context Protocol - Connects AI assistants to TabPFN via the Model Context Protocol for natural language predictions.
Adaptive Ensemble Scalers - Increases the number of ensemble members on wide datasets to ensure full feature coverage with a configurable cap.
Preprocessing Transform - Cycles each estimator through feature transformations to increase ensemble diversity and prediction robustness.
Test-Time Compute Improvers - Improves prediction accuracy by spending additional compute during fitting, without retraining the model.
Automated Feature Selection Tools - Filters large feature sets down to the most predictive ones to boost model performance on high-dimensional data.
Gradient-Based Fine-Tuning - Updates the transformer's weights via gradient descent on a user-provided dataset for domain adaptation.
Ensemble Learning - Combines multiple estimators with diverse feature subsets and preprocessing transforms for robust predictions.
Transformer Embedding Extraction - TabPFN extracts dense vector embeddings from a transformer model's internal representations for reuse in downstream tasks like clustering or visualization.
Tabular Embedding Extraction - TabPFN exports the model's internal embeddings from tabular data for use in downstream tasks or custom analysis.
Custom Data Fine-Tunings - Adapts the pretrained model to specialized domains or distribution shifts using your own dataset.
Generation Temperature Controls - Adjusts the softmax temperature to control the confidence spread of predicted probabilities for classification tasks.
Polynomial Feature Mapping - Creates pairwise feature interactions on demand to capture relationships that matter for prediction accuracy.
Inference Configuration Parameters - Configures inference parameters like output type, ensemble size, precision, and random seed for predictions.
Classification Fine-Tuning - Applies gradient-based fine-tuning to a pretrained transformer for classification tasks with validation splits.
Regression Fine-Tuning - Applies gradient-based fine-tuning to a pretrained transformer for regression tasks with validation splits.
Model Evaluation Metrics - TabPFN chooses a binary or multi-class metric such as ROC-AUC, PR-AUC, or log loss that matches the real-world consequences of prediction errors.
Hyperparameter Tuning - TabPFN selects optimal boosting rounds or other hyperparameters using cross-validation with early stopping, then refits on the full training set.
Tabular Model Parameter Tuners - Optimizes evaluation metric, softmax temperature, and class imbalance handling for a specific task.
Model Explainability - Interprets predictions through Shapley values, feature importance, and partial dependence plots for tabular data.
Sub-Millisecond Predictors - Returns predictions in under 0.2 milliseconds per row after a single fit call for near-instant inference.
Metric Optimization - Tunes decision thresholds and calibrates predictions to maximize a user-specified evaluation metric.
Managed Endpoint Inference - Invokes TabPFN models through Azure AI Foundry managed endpoints with scikit-learn interface.
Shapley Value Calculators - TabPFN attributes each prediction's deviation from the baseline to individual features using game-theoretic Shapley values to show which features drive the output.
Causally Constrained Data Generators - Restricts feature dependencies to a user-provided Directed Acyclic Graph so generated samples respect known causal relationships.
Energy Demand Forecasting - Forecasts energy consumption from tabular data for proactive load management and cost optimization.
Training Data Outlier Removers - Detects and removes extreme values from training data using a configurable threshold before model fitting.
Key-Value Cache Reuse - Caches transformer key-value states from training to accelerate subsequent predictions.
Domain-Specific Fine-Tuning - Adapts the pretrained model to a specialized domain or distribution shift by training further on the user's dataset.
Thinking Mode Engagers - Provides a thinking mode that applies extra computation during inference to boost accuracy on hard cases.
Domain Knowledge Feature Creators - Creates derived columns like ratios, interactions, or group aggregations to capture relationships the model cannot learn from raw data.
Quality Defect Detection - Detects defect patterns in manufacturing data to flag out-of-spec batches in real time.
Tabular Anomaly Detectors - Identifies outlier rows in tabular datasets by flagging samples with low probability under a learned distribution.
Capacity Extenders - Extends the model's built-in class limit to classify datasets with more classes than the default capacity.
Feature Count Reducers - Removes low-value or redundant columns to focus model attention on the most predictive attributes.
Dataset Uploads - Generates secure temporary URLs for uploading datasets directly to cloud storage, bypassing chat context limits.
Test-Time Compute Scalers - Spends additional compute during fitting to improve classification accuracy by up to 15%.
