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author | Dongjoon Hyun <dongjoon@apache.org> | 2016-02-22 09:52:07 +0000 |
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committer | Sean Owen <sowen@cloudera.com> | 2016-02-22 09:52:07 +0000 |
commit | 024482bf51e8158eed08a7dc0758f585baf86e1f (patch) | |
tree | e51f2c53b027178bb4e485d2781e266d96ff6e3d /docs/mllib-evaluation-metrics.md | |
parent | 1b144455b620861d8cc790d3fc69902717f14524 (diff) | |
download | spark-024482bf51e8158eed08a7dc0758f585baf86e1f.tar.gz spark-024482bf51e8158eed08a7dc0758f585baf86e1f.tar.bz2 spark-024482bf51e8158eed08a7dc0758f585baf86e1f.zip |
[MINOR][DOCS] Fix all typos in markdown files of `doc` and similar patterns in other comments
## What changes were proposed in this pull request?
This PR tries to fix all typos in all markdown files under `docs` module,
and fixes similar typos in other comments, too.
## How was the this patch tested?
manual tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #11300 from dongjoon-hyun/minor_fix_typos.
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> |