Unverified50% confidenceFactExact time
When using mean squared error (MSE) as the loss function, the negative gradient of the loss with respect to the predicted value is exactly equal to the residual y−prediction.
1
Sources
50%
Confidence
Long-term
Relevance
10/8/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedGradient descent computes the partial derivatives of the loss function with respect to model parameters and updates the parameters in the direction opposite to the gradient to approach the minimum of the loss function.75% similarUnverifiedUnder the assumption of a Gaussian output distribution, minimizing negative log-likelihood (NLL) and minimizing mean squared error (MSE) are mathematically completely equivalent.73% similarUnverified贝叶斯最优分类器在0-1损失下等价于选择后验概率最大的类别,回归问题中条件期望在平方损失下是最优预测72% similarUnverifiedThe mean squared error (MSE) cost function is defined as C(w,b) = 1/(2n) Σ ‖yᵢ − ŷᵢ‖², where each squared error term is always non-negative.71% similarUnverifiedWhen evaluating stock price predictions, directional accuracy and backtested returns are more meaningful than MSE or RMSE; a model with a very low RMSE but only 50% directional accuracy is ineffective or even loss-making in live trading.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/988619API
curl https://kongchang.com/api/v1/knowledge/claims/988619MCP
get_claim(id=988619)