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Diffstat (limited to 'docs')
| -rw-r--r-- | docs/mllib-evaluation-metrics.md | 16 |
1 files changed, 3 insertions, 13 deletions
diff --git a/docs/mllib-evaluation-metrics.md b/docs/mllib-evaluation-metrics.md index a269dbf030..c49bc4ff12 100644 --- a/docs/mllib-evaluation-metrics.md +++ b/docs/mllib-evaluation-metrics.md @@ -140,7 +140,7 @@ definitions of positive and negative labels is straightforward. #### Label based metrics Opposed to binary classification where there are only two possible labels, multiclass classification problems have many -possible labels and so the concept of label-based metrics is introduced. Overall precision measures precision across all +possible labels and so the concept of label-based metrics is introduced. Accuracy measures precision across all labels - the number of times any class was predicted correctly (true positives) normalized by the number of data points. Precision by label considers only one class, and measures the number of time a specific label was predicted correctly normalized by the number of times that label appears in the output. @@ -182,21 +182,11 @@ $$\hat{\delta}(x) = \begin{cases}1 & \text{if $x = 0$}, \\ 0 & \text{otherwise}. </td> </tr> <tr> - <td>Overall Precision</td> - <td>$PPV = \frac{TP}{TP + FP} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i - - \mathbf{y}_i\right)$</td> - </tr> - <tr> - <td>Overall Recall</td> - <td>$TPR = \frac{TP}{TP + FN} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i - + <td>Accuracy</td> + <td>$ACC = \frac{TP}{TP + FP} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i - \mathbf{y}_i\right)$</td> </tr> <tr> - <td>Overall F1-measure</td> - <td>$F1 = 2 \cdot \left(\frac{PPV \cdot TPR} - {PPV + TPR}\right)$</td> - </tr> - <tr> <td>Precision by label</td> <td>$PPV(\ell) = \frac{TP}{TP + FP} = \frac{\sum_{i=0}^{N-1} \hat{\delta}(\hat{\mathbf{y}}_i - \ell) \cdot \hat{\delta}(\mathbf{y}_i - \ell)} |