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Documentation Index

Fetch the complete documentation index at: https://docs.mathfi.ai/llms.txt

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Welcome to the MathFi.ai documentation, a platform to easily build predictive AI models from your CSV data and achieve outstanding prediction accuracy.

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Why MathFi.ai?

With MathFi.ai going from labelled data to accurate predictions is easier than ever:
  • Create datasets from CSV files
  • Get models trained in minutes
  • Obtain high accuracy predictions using these models on unseen data

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What is MathFi.ai?

MathFi.ai is a production-ready predictive AI platform for tabular classification on imbalanced data. It uses Cellular Balanced Learning (CBL), a proprietary training approach built to solve the class imbalance problem that causes standard ML methods to underperform on fraud detection, anomaly detection, insurance claims, and rare-event prediction tasks. Key differentiators:
  • No GPU required: training runs entirely on commodity CPUs, cutting infrastructure costs compared to typical AutoML platforms
  • Fast training: eight competing algorithmic pipelines converge to your target performance in minutes for most well-prepared datasets
  • Minimal configuration: three hyperparameters control the entire training process (number of buckets, scaling factor, performance threshold)
  • API-first: a predictive ML API that integrates into existing data pipelines or powers new prediction applications
  • Proven across industries: financial services, insurance, healthcare, and any domain with imbalanced tabular datasets
Upload a labelled CSV, train a model, run inference. No infrastructure setup required.

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Who is MathFi.ai for?

MathFi.ai may be suitable for:
  • In general, organisations looking to get insights on their existing data, getting started on their way to obtain meaningful predictions relevant to their business
  • Data Science teams, to benchmark or complement their existing model development with a powerful platform to add to their existing toolchain
  • Researchers and professionals looking to obtain reliable predictions on datasets they’re collecting or building for a specific field (Healthcare, Fraud, …)
To get started request early access via Request access form.

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How does it work?

As a quick glance, this is how MathFi.ai platform works: Overview
  • Users prepare a tabular, labelled data CSV file with a set of features and an outcome (guide)
  • This input CSV is uploaded to the platform and a Dataset is created
  • This Dataset is used to Train a Model
  • The training process consists of a set of proprietary algorithms that compete with each other to achieve a given target performance. See glossary for metrics used during training. A Model is created from the output of the winner algorithm
    • 90% of labelled data is used for training, remaining 10% is stripped out of its labels and used for testing as part of the overall training process
    • Training process runs fast (minutes or few hours) in most of the cases when the data is well prepared. MathFi.ai uses novel training algorithms that converge to the target performance or stall/stop way faster than other known classification methods
    • If training stalls, times out or fails for any reason, the model won’t be created
    • Models won’t be created if the newest achieved performance is not greater than existing for the given dataset
  • Once there’s an initial model created (champion model), predictions (inference) can be run by uploading an unseen data CSV. The trained model is used to accurately predict the unseen data and obtain a downloadable prediction result CSV output

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Use MathFi.ai

  • Log into the platform and follow the Quickstart to test your access and start using the dashboard
  • You can also directly start using your own data to get predictions:
    • Format labelled and unseen CSV data for training and prediction following the Input CSV Creation guide
    • Create datasets, train models and get initial predictions following the Training guide
    • Gradually improve the performance of your models following the Hyperparameter tuning guide
  • Eventually, integrate MathFi.ai within your existing workflows or create brand new apps using it as a base prediction engine

Getting Started

Follow the quickstart guide to make your first prediction

API Reference

Integrate MathFi.ai into your existing workflows

Guides

In-depth guides for datasets, training and predictions

Use Cases

Real-world examples across industries