Supported models

Published

January 6, 2025

The ValidMind Library provides out-of-the-box support for testing and documentation for an array of model types and modeling packages.

What is a supported model?

A supported model refers to a model for which predefined testing or documentation functions exist in the ValidMind Library, provided that the model you are developing is documented using a supported version of our library. These model types cover a very large portion of the models used in commercial and retail banking.

ValidMind does not limit our users to specific model types.
  • The ValidMind Library is extensible to support future model types or modeling packages to accomodate rapid developments in the AI space, including the advent of large language models (LLMs).
  • You always have the flexibility to implement custom tests and integrate external test providers.1

Supported model types

Vendor models
ValidMind offers support for both first-party models and third-party vendor models.

Traditional statistical models

Linear regression

Models relationship between a scalar response and one or more explanatory variables.

Logistic regression

Models relationship between a scalar response and one or more explanatory variables.

Time series

Analyzes data points collected or sequenced over time.

Machine learning models

Hugging Face-compatible models

  • Natural language processing (NLP) text classification — Categorizes text into predefined classes.
  • Tabular classification — Assigns categories to tabular dataset entries.
  • Tabular regression — Predicts continuous outcomes from tabular data.

Neural networks

  • Long short-term memory (LSTM) — Processes sequences of data, remembering inputs over long periods.
  • Recurrent neural network (RNN) — Processes sequences by maintaining a state that reflects the history of processed elements.
  • Convolutional neural network (CNN) — Primarily used for processing grid-like data such as images.

Tree-based models
(XGBoost / CatBoost / random forest)

  • Classification — Predicts categorical outcomes using decision trees.
  • Regression — Predicts continuous outcomes using decision trees.

K-nearest neighbors (KNN)

  • Classification — Assigns class by majority vote of the k-nearest neighbors.
  • Regression — Predicts value based on the average of the k-nearest neighbors.

Clustering

  • K-means — Partitions n observations into k clusters in which each observation belongs to the cluster with the nearest mean.

Generative AI models

Large language models (LLMs)

  • Classification — Categorizes input into predefined classes.
  • Text summarization — Generates concise summaries from longer texts.

Supported modeling libraries and other tools

  • scikit-learn — A Python library for machine learning, offering a range of supervised and unsupervised learning algorithms.

  • statsmodels — A Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and exploring data.

  • PyTorch — An open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.

  • Hugging Face Transformers — Provides thousands of pre-trained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, and text generation.

  • XGBoost — An optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable, implementing machine learning algorithms under the Gradient Boosting framework.

  • CatBoost — An open-source gradient boosting on decision trees library with categorical feature support out of the box, for ranking, classification, regression, and other ML tasks.

  • LightGBM — A fast, distributed, high-performance gradient boosting (GBDT, GBRT, GBM, or MART) framework based on decision tree algorithms, used for ranking, classification, and many other machine learning tasks.

  • R models, via rpy2 - R in Python — Facilitates the integration of R’s statistical computing and graphics capabilities with Python, allowing for R models to be called from Python.

  • Large language models (LLMs), via OpenAI-compatible APIs — Access to advanced AI models trained by OpenAI for a variety of natural language tasks, including text generation, translation, and analysis, through a compatible API interface. This support includes both the OpenAI API and the Azure OpenAI Service via API.

What’s next