newdigital

Glass-box algorithms are machine learning algorithms that are fully transparent and inherently intelligible. They defy conventional wisdom that there is a tradeoff between prediction accuracy and interpretability in Machine Learning because they offer an unparalleled level of accuracy and transparency. This means they are exponentially easier to debug, maintain, and improve upon iteration when compared to their black-box alternatives that we are more familiar with. Black-box models are all machine learning models whose inner workings are complex and not easily interpretable. These models can represent high dimensional and non-linear relationships which aren't easily understood by us as humans.

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