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Merge pull request #371 from ajkl/patch-2

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fixing some typos
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Tianqi Chen 2015-07-03 21:42:54 -07:00
commit 53a18635ee
1 changed files with 3 additions and 3 deletions
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demo/binary_classification/README.md
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@ -147,7 +147,7 @@ Run the command again, we can find the log file becomes
``` ```
The rule is eval[name-printed-in-log] = filename, then the file will be added to monitoring process, and evaluated each round. The rule is eval[name-printed-in-log] = filename, then the file will be added to monitoring process, and evaluated each round.
xgboost also support monitoring multiple metrics, suppose we also want to monitor average log-likelihood of each prediction during training, simply add ```eval_metric=logloss``` to configure. Run again, we can find the log file becomes xgboost also supports monitoring multiple metrics, suppose we also want to monitor average log-likelihood of each prediction during training, simply add ```eval_metric=logloss``` to configure. Run again, we can find the log file becomes
``` ```
[0] test-error:0.016139 test-negllik:0.029795 trainname-error:0.014433 trainname-negllik:0.027023 [0] test-error:0.016139 test-negllik:0.029795 trainname-error:0.014433 trainname-negllik:0.027023
[1] test-error:0.000000 test-negllik:0.000000 trainname-error:0.001228 trainname-negllik:0.002457 [1] test-error:0.000000 test-negllik:0.000000 trainname-error:0.001228 trainname-negllik:0.002457
@ -166,7 +166,7 @@ When you are working with a large dataset, you may want to take advantage of par
#### Additional Notes #### Additional Notes
* What are ```agaricus.txt.test.buffer``` and ```agaricus.txt.train.buffer``` generated during runexp.sh? * What are ```agaricus.txt.test.buffer``` and ```agaricus.txt.train.buffer``` generated during runexp.sh?
- By default xgboost will automatically generate a binary format buffer of input data, with suffix ```buffer```. When next time you run xgboost, it detects i - By default xgboost will automatically generate a binary format buffer of input data, with suffix ```buffer```. Next time when you run xgboost, it will detects these binary files.
Demonstrating how to use XGBoost accomplish binary classification tasks on UCI mushroom dataset http://archive.ics.uci.edu/ml/datasets/Mushroom