Unverified50% confidenceFactExact time
The loss function (cross-entropy) of logistic regression is essentially negative log-likelihood.
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Long-term
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9/4/2026
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VerifiedThe cross-entropy loss in classification tasks is essentially the negative of the log-likelihood function, and minimizing cross-entropy is equivalent to maximizing the log-likelihood of the model on the training data.77% similarUnverified交叉熵损失函数 H(p,q) = -Σp(x)log q(x) 的最小值在q=p时取到，等于真实分布的熵73% similarUnverified微分熵可以取负值、不具有坐标不变性，在非线性变量变换下会发生改变73% similarUnverified线性回归的最小二乘损失与逻辑回归的交叉熵损失分别对应高斯噪声假设与伯努利分布假设下的最大似然估计72% similarUnverifiedAssuming the data follows a Bernoulli or multinomial distribution, maximizing the likelihood is equivalent to minimizing the cross-entropy loss function.68% similar
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