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
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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
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8 years ago
Haystacks and Needles: Anomaly Detection

 Anomalies vs Outliers Anomaly detection, or finding needles in a haystack, is an important tool in data exploration and unsupervised analytic modeling. Anomaly detection also creates a path to supervised modeling by singling out key examples that an analyst can begin to classify as needles or hay. Those labeled examples are essential for supervised learning, which is much more powerful than unsupervised learning methods like clustering. Though anomaly and outlier are often used interchangeably we’d like to emphasize distinct definitions. As Ravi Parikh describes well in a blog post[1], “An outlier is a legitimate data point that’s far

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