Lingaku- cross entropy (information theory)
Generated definitions (experimental)
- cross entropy (information theory)
This refers to a measure of difference between two probability distributions, often used in machine learning and statistics to quantify the performance of classification models.
In machine learning, cross entropy is often used to evaluate the predictive performance of models.
A larger dataset usually helps to reduce cross entropy.
Generated collocations (experimental)
jiāo chā shāng交叉熵sǔn shī损失cross entropy losszuì xiǎo huà最小化jiāo chā shāng交叉熵minimizing cross entropyjiāo chā交叉shāng熵suàn fǎ算法cross entropy algorithmjiāo chā交叉shāng熵dù度liáng量cross entropy metricjiāo chā交叉shāng熵yōu huà优化cross entropy optimizationjiāo chā shāng交叉熵fēn lèi分类cross entropy classificationmó xíng模型jiāo chā shāng交叉熵model cross entropyjiāo chā交叉shāng熵hán函shǔ数cross entropy functionjiāo chā交叉shāng熵wù误chā差cross entropy errorjiāo chā shāng交叉熵zhǐ biāo指标cross entropy indicator