Machine Learning Times
Machine Learning Times
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Visualizing Decision Trees with Pybaobabdt
 Originally published in Towards Data Science, Dec 14, 2021....
Correspondence Analysis: From Raw Data to Visualizing Relationships
 Isn’t it satisfying to find a tool that makes...
Podcast: Four Things the Machine Learning Industry Must Learn from Self-Driving Cars
    Welcome to the next episode of The Machine...
A Refresher on Continuous Versus Discrete Input Variables
 How many times have I heard that the most...
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3 years ago
Why Operationalizing Machine Learning Requires a Shrewd Business Perspective

 Originally published in Analytics Magazine For a rocket scientist, the math isn’t the hardest part. What’s hard is being so often misunderstood. The same goes for data scientists, who time and again lack the support needed to successfully launch the fruits of their brilliant labor into action. These math heads have got to integrate into the organization as a whole, lest they vanish into the obscurities of their analysis. Their isolation is an enemy to their usefulness. After all, the most wicked and pervasive pitfall of predictive analytics is organizational in nature, not technical: Predictive models often fail

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