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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.
Word embeddings are vector representations of words, where more similar words will have similar locations in vector space. First developed by a team of researchers at Google led by Thomas Mikolov, and discussed in the paper Efficient Estimation of Word Representations in Vector Space, word2vec is a popular group of models that produce word embeddings by training shallow neural networks. In this blog post, we apply a word2vec model to the Alteryx Community texts to develop Alteryx-specific word embeddings.