How-to Guides¶
Warning
The How Tos are WIP and are expected to be heavily modified in the future
These guides demonstrate more in-depth features and customizations of DataEval features for more advanced users.
In addition to viewing them in our documentation, these notebooks can also be opened in Google Colab to be used interactively!
Detectors¶
The purpose of these tools is to identify or detect issues within a dataset. The guides below exemplify powerful solutions to common problems in ML.
Identify outliers and anomalies with clustering algorithms |
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Identify and remove duplicates from a PyTorch Dataset |
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Find negatively impactful images in multiple backgrounds |
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Metrics¶
Metrics are a set of tools that measure and analyze data. The guides below show best practices when solving common ML problems.
Calculate feasibility of performance requirements on different datasets using Bayes Error Rate (BER) |
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Display data distributions between 2 datasets |
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Compare label distributions between 2 datasets |
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Detect undersampled subsets of datasets |
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Apply DataEval’s statistical outputs to
DataEval’s |
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Workflows¶
Workflows are end-to-end processes that detect, measure, and analyze data against requirements. The guides below help you solve common problems found across machine learning tasks.
Determine the amount of data needed to meet image classification performance requirements |
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Models¶
DataEval uses models during all stages of the ML Lifecycle. The guides below show specific examples on model usage at different levels of expertise.
Train and evaluate an autoencoder to generate effective image embeddings for downstream tasks |
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