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Active Machine Learning with Python

Active Machine Learning with Python

By : Margaux Masson-Forsythe
3.5 (2)
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Active Machine Learning with Python

Active Machine Learning with Python

3.5 (2)
By: Margaux Masson-Forsythe

Overview of this book

Building accurate machine learning models requires quality data—lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by Margaux Masson-Forsythe, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train robust models with just a fraction of the data using Python's powerful active learning tools. You’ll master the fundamental techniques of active learning, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active learning algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine learning techniques, you’ll learn how to enhance the performance of computer vision models like image classification, object detection, and semantic segmentation and delve into a machine AL method for selecting the most informative frames for labeling large videos, addressing duplicated data. You’ll also assess the effectiveness and efficiency of active machine learning systems through performance evaluation. By the end of the book, you’ll be able to enhance your active learning projects by leveraging Python libraries, frameworks, and commonly used tools.
Table of Contents (13 chapters)
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Part 1: Fundamentals of Active Machine Learning
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Part 2: Active Machine Learning in Practice
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Part 3: Applying Active Machine Learning to Real-World Projects
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Index
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Handling model-label disagreements

Disagreements between model predictions and human labels are inevitable. In this section, we will study how to identify and resolve conflicts.

Programmatically identifying mismatches

To identify discrepancies between the model’s predictions and the human-annotated labels, we can write some simple Python code that highlights the mismatches for review.

Let’s consider the example of an NLP sentiment classifier. This type of classifier is designed to analyze and understand the sentiment or emotions expressed in text. By examining the words, phrases, and context used in a given piece of text, an NLP sentiment classifier can determine whether the sentiment is positive, negative, or neutral. First, we will use the sentiment-analysis model from Huggingface:

sentiment_pipeline = pipeline("sentiment-analysis")
data = ["I love you", "I hate you"]
sentiment_pipeline(data)

The returns the following output...

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