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5 Things to Know about Linear Regression

Before we get into talking about linear regression, you may recall that we recently have discussed advanced machine learning techniques such as neural networks and support vector machines, but these are not always the most appropriate tool for modeling data. Machine Learning models are big, complicated, and almost impossible to interpret. While they have great capacity and are sometimes the only solution to difficult problems, their downsides can be substantial…
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Combining the Best of Both Worlds: Machine Learning & Rule-Based Approach

In the past few years, more and more organizations have started relying solely on Machine Learning (also called Artificial Intelligence or AI) for addressing Text Analytics tasks. The reasons are that this approach is faster, suitable for large volumes of data, and more adaptable to new contexts, as it does not require hard-coded rules. However, AI / Machine Learning algorithms are only as good as the data they are trained…
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Wide, Bulky Data and Dimensionality Reduction

As the saying goes, “mo’ data, mo’ problems.” Well, that’s not quite the saying and that’s not quite what we mean, but it certainly is true that large data comes with some problems that need to be solved. There are two ways that data can be “big.” Traditionally, Big Data is considered large literally by its physical size in terms of the memory space it consumes. In this article, we’re…
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How to Audit and Explore Datasets in PolyAnalyst

Suppose you just obtained a fresh new set of data. You might know what it’s generally about, but the only way you can know what hypotheses to test (and what analysis challenges you’re up against) is to explore the data and audit its overall quality. Unless you can see the basic overview of what you’re dealing with, you won’t be able to determine the most appropriate models to build or…
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