ETR 0.5
Fully trazable & explainable
ML Technology

Input Variables:
ETR 0.5 handles a large and dynamic set of input variables without the need for retraining. This is especially useful in environments where the data features are constantly evolving or where new variables are discovered over time.

Unlike neural networks or decision trees, which may struggle with scalability or require full retraining to accommodate new inputs, ETR 0.5 can natively support an expanding input space.

Computational Resources:
ETR 0.5 is resource-efficient both in training and operation. It supports modular updates without the heavy computational cost of retraining whole models from scratch.

This makes it well-suited for environments with limited hardware or strict energy/resource budgets (e.g., embedded systems or edge devices), unlike many traditional ML models that require large-scale GPU clusters.

Maximum Response Time :
Designed for deployment in decision-critical systems (e.g., finance, aerospace, or justice), ETR 0.5 is optimized for fast, deterministic response times once trained.

It enables real-time decision-making with a traceable logic path, offering significant advantages over black-box models like deep neural networks, where inference speed can be high but explainability is low.

Degrees of Freedom:
Degrees of freedom refer to the variety and complexity of outputs or actions the model must handle. ETR 0.5 supports high-dimensional output spaces and can manage complex multi-variable decision scenarios.

With its cognitive architecture, ETR 0.5 can set objectives, evaluate strategies, and generate alternatives, providing intelligent action across varied contexts—something traditional decision trees or static rule-based systems are not designed for.