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Fix broken links. (#6455)

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Co-authored-by: Hao Ziyu <haoziyu@qiyi.com>
Co-authored-by: fis <jm.yuan@outlook.com>
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hzy001
2020-12-02 17:39:12 +08:00
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commit c2ba4fb957
4 changed files with 4 additions and 7 deletions
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3
demo/CLI/binary_classification/README.md
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@@ -62,7 +62,7 @@ test:data = "agaricus.txt.test"
We use the tree booster and logistic regression objective in our setting. This indicates that we accomplish our task using classic gradient boosting regression tree(GBRT), which is a promising method for binary classification.
The parameters shown in the example gives the most common ones that are needed to use xgboost.
If you are interested in more parameter settings, the complete parameter settings and detailed descriptions are [here](../../doc/parameter.rst). Besides putting the parameters in the configuration file, we can set them by passing them as arguments as below:
If you are interested in more parameter settings, the complete parameter settings and detailed descriptions are [here](https://xgboost.readthedocs.io/en/stable/parameter.html). Besides putting the parameters in the configuration file, we can set them by passing them as arguments as below:
```
../../xgboost mushroom.conf max_depth=6
@@ -161,4 +161,3 @@ Eg. ```nthread=10```
Set nthread to be the number of your real cpu (On Unix, this can be found using ```lscpu```)
Some systems will have ```Thread(s) per core = 2```, for example, a 4 core cpu with 8 threads, in such case set ```nthread=4``` and not 8.

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demo/CLI/regression/README.md
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@@ -1,6 +1,6 @@
Regression
====
Using XGBoost for regression is very similar to using it for binary classification. We suggest that you can refer to the [binary classification demo](../binary_classification) first. In XGBoost if we use negative log likelihood as the loss function for regression, the training procedure is same as training binary classifier of XGBoost.
Using XGBoost for regression is very similar to using it for binary classification. We suggest that you can refer to the [binary classification demo](../binary_classification) first. In XGBoost if we use negative log likelihood as the loss function for regression, the training procedure is same as training binary classifier of XGBoost.
### Tutorial
The dataset we used is the [computer hardware dataset from UCI repository](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware). The demo for regression is almost the same as the [binary classification demo](../binary_classification), except a little difference in general parameter:
@@ -14,4 +14,3 @@ objective = reg:squarederror
```
The input format is same as binary classification, except that the label is now the target regression values. We use linear regression here, if we want use objective = reg:logistic logistic regression, the label needed to be pre-scaled into [0,1].