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Performance Metrics - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= Performance metrics in machine learning are quantitative measures used to evaluate the effectiveness and efficiency of a machine learning model. These metrics provide insights into how well a model is performing on a given task and help assess its generalization capabilities. The choice of performance metrics depends on the specific nature of the problem being solved, as different tasks may require different evaluation criteria. Some common performance metrics in machine learning are: '
The choice of which metric to use depends on the specific goals and characteristics of the machine learning task. For example, in a medical diagnosis task, high recall might be more important to minimize false negatives, even at the cost of increased false positives. In fraud detection, precision might be more critical to minimize false positives. Performance metrics are easier to understand and are directly connected to business goals, which are benefits of Performance metrics over loss functions. Note that bias, see page3352, can exist in performance metrics, e.g. accuracy versus balanced accuracy: Using simple accuracy as a performance metric might be misleading in imbalanced datasets. For instance, if one class significantly outnumbers another, a model might appear to perform well by simply predicting the majority class more frequently. Mitigation of bias:
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