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type: concept
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    - featurization-for-transfer-learning-databricks-on-aws.md
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  aliases:
    - truncated-model-feature-extraction
    - TMFE
    - model-truncation-for-feature-extraction
    - MTFFE
    - pre-trained-model-truncation-for-feature-extraction
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  citations:
    - file: featurization-for-transfer-learning-databricks-on-aws.md
title: Truncated Model Feature Extraction
description: The technique of removing the final classification layers from a pre-trained deep learning model so the modified model outputs a feature tensor instead of a prediction.
tags:
  - deep-learning
  - model-architecture
  - feature-engineering
timestamp: "2026-06-18T12:19:54.695Z"
---

---
title: Truncated Model Feature Extraction
summary: A technique in transfer learning where a pre-trained deep learning model is truncated by removing its final prediction layers to produce a feature vector for a new downstream task.
source: featurization-for-transfer-learning-databricks-on-aws.md
kind: concept
createdAt: "2026-06-18T12:31:21.399Z"
updatedAt: "2026-06-18T12:31:21.399Z"
tags:
  - deep-learning
  - transfer-learning
  - feature-engineering
  - databricks
aliases:
  - truncated-model-feature-extraction
  - tmfe
confidence: 0.9
provenanceState: extracted
inferredParagraphs: 0
---

# Truncated Model Feature Extraction

**Truncated Model Feature Extraction** is a transfer learning technique that converts a pre-trained deep neural network into a feature extractor by removing its final classification or prediction layers. The truncated model outputs a tensor of high-level features, which can then be used as input to train a separate, simpler model on a new dataset or for a different problem domain. ^[featurization-for-transfer-learning-databricks-on-aws.md]

## Overview

Pre-trained deep learning models (e.g., image classifiers from `tensorflow.keras.applications`) have learned rich representations of the data they were trained on. By removing the last layer or layers, the model no longer produces a class prediction but instead outputs a feature vector that captures those learned representations. This truncation step is a form of featurization and is a simple yet powerful method for [Transfer Learning](/concepts/transfer-learning.md). ^[featurization-for-transfer-learning-databricks-on-aws.md]

## Workflow

The typical workflow for truncated model feature extraction is:

1. **Start with a pre-trained model** — Use a state-of-the-art model, such as an image classification model from `tensorflow.keras.applications`.
2. **Truncate the model** — Remove the final prediction layers (e.g., the softmax layer and any preceding dense layers). The modified model now outputs a feature tensor instead of a class label.
3. **Apply the truncated model to a new dataset** — Run the model on a dataset from a different problem domain, using pandas UDFs or [Scalar Iterator pandas UDFs](/concepts/scalar-iterator-pandas-udfs.md) to distribute the computation across a cluster.
4. **Use the extracted features to train a new model** — The feature vectors serve as input to a simpler downstream model (e.g., logistic regression). ^[featurization-for-transfer-learning-databricks-on-aws.md]

## Supported Libraries and Execution

Databricks supports featurization at scale using deep learning libraries included in [Databricks Runtime ML](/concepts/databricks-runtime-ml.md), such as TensorFlow and PyTorch. The computation can be distributed across a cluster using pandas UDFs for high performance and flexible APIs. ^[featurization-for-transfer-learning-databricks-on-aws.md]

## Related Concepts

- [Transfer Learning](/concepts/transfer-learning.md) — Reusing knowledge from one domain in a related domain; featurization is a key method for transfer learning.
- [Pandas UDFs](/concepts/scalar-iterator-pandas-udfs.md) — User-defined functions that enable featurization at scale.
- [Databricks Runtime ML](/concepts/databricks-runtime-ml.md) — The machine learning runtime environment that includes deep learning libraries.
- [Feature Engineering](/concepts/featureengineeringclient-api.md) — The process of creating or transforming features for downstream models.

## Sources

- featurization-for-transfer-learning-databricks-on-aws.md

# Citations

1. [featurization-for-transfer-learning-databricks-on-aws.md](/references/featurization-for-transfer-learning-databricks-on-aws-3a0869f4.md)
