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Discrete feature: Difference between revisions
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Machine learning uses features to determine things such as whether an email is spam or not, or what kind of illness someone might have. Some features are called "discrete," meaning they can only be one among several options - like hair color or occupation - so we need to turn those choices into numbers so the computer can comprehend them, by giving each choice its own number or using 1's and 0's for representation. Discrete features are beneficial in that they help us better comprehend things but it is important not to overuse them or we might get incorrect answers. | Machine learning uses features to determine things such as whether an email is spam or not, or what kind of illness someone might have. Some features are called "discrete," meaning they can only be one among several options - like hair color or occupation - so we need to turn those choices into numbers so the computer can comprehend them, by giving each choice its own number or using 1's and 0's for representation. Discrete features are beneficial in that they help us better comprehend things but it is important not to overuse them or we might get incorrect answers. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | ||