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Deploying Custom Python Models with Alteryx Promote

For years, data scientists have struggled to deploy their models in a timely manner before they become obsolete. Traditionally, models must be manually recoded, a time-intensive process that can take months, if not longer, to complete. Alteryx Promote solves this model deployment challenge by allowing data scientists to quickly turn complex Machine Learning models into a RESTful API from the development environment of their choice.

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Simple is Best: Occam's Razor in Data Science

A guiding principle in scientific fields and general problem solving is Occam’s razor (also known as the law of parsimony). Credited to 14th-century friar William Ockham, all that Occam’s razor states is "simple solutions are more likely to be correct than complex ones." Razor refers to the process of distinguishing between two hypotheses by “shaving away” any unnecessary assumptions.

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Bias Versus Variance

There are two types of model errors when making an estimate; bias and variance. Understanding both of these types of errors, as well as how they relate to one another is fundamentally important to understanding model overfitting, underfitting, and complexity. Various sources of error can lead to bias and variance in a model. Understanding how these sources of error help us improve the data fitting process, resulting in more accurate models.

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Filling in the Blanks: An Introduction to Spatial Interpolation

When there are missing values in a typical data set, you have a few options on how to handle them. You can create a new category for the missing values, you can remove the observations with missing values, or you can interpolate values for the missing observations. But what about spatial data? What if you have a spatial data set for a continuous feature (e.g., annual rainfall), but that data set doesn't include a value for a point that you need.