机器学习笔记
分类
准确率:所有样本中预测正确的占比
accuracy =\frac {TP+TN}{TP+TN+FP+FN} = \frac {T}{T+F}
精确率:预测为正的样本中真正的正样本占比
precision = \frac {TP}{TP+FP} = \frac {TP}{P'}
召回率:正样本中预测为正的占比
recall = \frac{TP}{TP+FN} = \frac{TP}{P}
F1:精确率和召回率的调和均值
\begin{align*}
\frac{2}{F_1} & = \frac{1}{precision} + \frac{1}{recall}\cr
F_1 & = \frac{2\cdot precision \cdot recall}{precision+recall}\cr
F_1 & = \frac{2TP}{2TP + FP + FN} \cr
F_1 & = \frac{2TP}{P' + P} \cr
\end{align*}
F-score:
F_{score}=(1+\beta^2)\cdot \frac{precision \cdot recall}{\beta^2\cdot precision + recall}
| P | N | |
|---|---|---|
| P' | TP | FP |
| N' | FN | TN |
序列
BLEU(Bilingual Evaluation understudy)
CP_n(C,S)=\frac {\sum_i\sum_k\min(h_k(c_i),max_{j \in m}h_k(s_{ij}))}{\sum_i\sum_kh_k(c_j)}
惩罚因子BP(Brevity Penalty)
b(C,S)=\begin{cases}
1, &l_c \lt l_s \cr
e^{1-\frac{l_s}{l_c}}, &l_c \geq l_s
\end{cases}
BLEU_N(C,S)=b(C,S)\exp(\sum_{n=1}^N\omega_n\log CP_n(C,S))
机器翻译
ROUGE(Recall-Oriented Understudy for Gisting Evaluation)
| ROUGE-N | 基于N-gram公现性统计 |
|---|---|
| ROUGE-L | 基于最长公有子句共现性精确度和召回率Fmeasure统计 |
| ROUGE-W | 代权重的最长公有子句共现性精确度和召回率Fmeasure统计 |
| ROUGE-S | 不连续二元组共现性精确度和召回率Fmeasure统计 |
ROUGE-N
ROUGE-N=\frac {\sum_{S \in ReferencesSummaries}\sum_{gram_n\in S}Count_{match}(gram_n)}
{\sum_{S \in ReferencesSummaries}\sum_{gram_n\in S}Count(gram_n)}
ROUGE-L 最长公共子句longest common subsequence(LCS)
R_{lcs}=\frac {LCS(X,Y)}{m} ,m=len(X)
P_{lcs}=\frac {LCS(X,Y)}{n} ,n=len(Y)
F_{lcs}=\frac{(1+\beta^2)R_{lcs}P_{lcs}}{R_{lcs}+\beta^2P_{lcs}}