Learning is unsupervised when AI algorithms are given unlabeled data and must make sense of it without any instruction. Such machines “teach themselves” what result to produce. The algorithm looks for structure in the training data, like finding which examples are similar to each other and grouping them into clusters.
Variable importance is a metric that indicates how much an independent variable contributes to predictions in a model. The higher the value shown for a variable in its ranking, the more important it is to the model generated.
Understanding the significance of predictors provides insights for interpreting results, and also may be useful for improving model quality. For instance, editing data sets to rationalize incorrect or incomplete columns — or removing irrelevant ones — can make models faster and more accurate.
The current state of AI, which does single tasks like playing games recognize images, or predicting outcomes. This is as opposed to Strong AI, also known as Artificial General Intelligence (AGI), which could do anything that humans do.