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Diffstat (limited to 'docs/mllib-evaluation-metrics.md')
| -rw-r--r-- | docs/mllib-evaluation-metrics.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/mllib-evaluation-metrics.md b/docs/mllib-evaluation-metrics.md index 774826c270..a269dbf030 100644 --- a/docs/mllib-evaluation-metrics.md +++ b/docs/mllib-evaluation-metrics.md @@ -67,7 +67,7 @@ plots (recall, false positive rate) points. </thead> <tbody> <tr> - <td>Precision (Postive Predictive Value)</td> + <td>Precision (Positive Predictive Value)</td> <td>$PPV=\frac{TP}{TP + FP}$</td> </tr> <tr> @@ -360,7 +360,7 @@ $$I_A(x) = \begin{cases}1 & \text{if $x \in A$}, \\ 0 & \text{otherwise}.\end{ca **Examples** -The following code snippets illustrate how to evaluate the performance of a multilabel classifer. The examples +The following code snippets illustrate how to evaluate the performance of a multilabel classifier. The examples use the fake prediction and label data for multilabel classification that is shown below. Document predictions: @@ -558,7 +558,7 @@ variable from a number of independent variables. <td>$RMSE = \sqrt{\frac{\sum_{i=0}^{N-1} (\mathbf{y}_i - \hat{\mathbf{y}}_i)^2}{N}}$</td> </tr> <tr> - <td>Mean Absoloute Error (MAE)</td> + <td>Mean Absolute Error (MAE)</td> <td>$MAE=\sum_{i=0}^{N-1} \left|\mathbf{y}_i - \hat{\mathbf{y}}_i\right|$</td> </tr> <tr> |