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August 11, 2019 Aviv Nutovitz Data Science

Demystifying Feature Selection: Filter vs Wrapper Methods

Feature selection algorithms are increasingly growing in significance. In this article, we will cover (and compare) two popular feature selection methodologies - Filter and Wrapper.

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July 24, 2019 Maël Fabien Data Science

Interpretability and explainability (Part 2)

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

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.

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 16, 2019 Maor Shlomo Automation

Why automating data science will kill the BI industry

Machine learning models are mathematical models that leverage historical data to uncover patterns which can help predict the future to a certain degree of accuracy. And

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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