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July 15, 2019 Maël Fabien Data Science

Interpretability and explainability (Part 1)

The whole idea behind interpretable and explainable ML is to avoid the black box effect.

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July 11, 2019 Maor Shlomo Data Science

The ultimate machine learning model deployment checklist

While there is some room for error while integrating models into production environments, there is also a very good probability that these issues will eventually lead to disaster. And that’s exactly why we have created this pre-model deployment checklist.

July 9, 2019 Maël Fabien Data Enrichment

Who’s the painter?

Better features, better data

June 23, 2019 Omer Har Data Science

The spectrum of complexity

Demystifying the old battle between transparent, explainable models and more accurate, complex models.

June 22, 2019 Eilon Baer Feature Engineering

How feature selection could actually harm your machine learning models when used incorrectly

(or – “how I f***ed up my text classifier while thinking it’s performing well")

Categories
  • All posts
  • Automation
  • Business Intelligence
  • Data Enrichment
  • Data Science
  • Explainability
  • Feature Engineering
  • Feature Selection
  • General
  • Interpretability
  • Machine Learning
  • Predictive Models
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