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In machine studying purposes, practitioners should take note of the fee related to the algorithm. These prices embrace:
Value of buying coaching data
Value of data annotation/labeling and cleansing
Computational value for mannequin becoming, validation, and testing
Value of accumulating options/attributes for check data
Value of consumer suggestions assortment
Value of incorrect prediction/classification
Cost-Sensitive Machine Learning is among the first books to offer an summary of the present analysis efforts and issues on this space. It discusses actual-world purposes that incorporate the price of studying into the modeling course of.
The primary a part of the book presents the theoretical underpinnings of value-delicate machine studying. It describes nicely-established machine studying approaches for decreasing data acquisition prices throughout coaching in addition to approaches for decreasing prices when techniques should make predictions for brand spanking new samples. The second half covers actual-world purposes that successfully commerce off several types of prices. These purposes not solely use conventional machine studying approaches, however additionally they incorporate slicing-edge analysis that advances past the constraining assumptions by analyzing the appliance wants from first rules.
Spurring additional analysis on a number of open issues, this quantity highlights the usually implicit assumptions in machine studying methods that weren’t absolutely understood prior to now. The book additionally illustrates the business significance of value-delicate machine studying by way of its protection of the speedy software developments made by main corporations and educational analysis labs.
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