Fix minor typos (#2842)
* Some minor changes to the code style Some minor changes to the code style in file basic_walkthrough.py * coding style changes * coding style changes arrcording PEP8 * Update basic_walkthrough.py * Fix minor typo * Minor edits to coding style Minor edits to coding style following the proposals of PEP8.
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Binary Classification
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=====================
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This is the quick start tutorial for xgboost CLI version.
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Here we demonstrate how to use XGBoost for a binary classification task. Before getting started, make sure you compile xgboost in the root directory of the project by typing ```make```
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The script runexp.sh can be used to run the demo. Here we use [mushroom dataset](https://archive.ics.uci.edu/ml/datasets/Mushroom) from UCI machine learning repository.
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Here we demonstrate how to use XGBoost for a binary classification task. Before getting started, make sure you compile xgboost in the root directory of the project by typing ```make```.
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The script 'runexp.sh' can be used to run the demo. Here we use [mushroom dataset](https://archive.ics.uci.edu/ml/datasets/Mushroom) from UCI machine learning repository.
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### Tutorial
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#### Generate Input Data
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# L2 regularization term on weights, default 0
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lambda = 0.01
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# L1 regularization term on weights, default 0
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f ```agaricus.txt.test.buffer``` exists, and automatically loads from binary buffer if possible, this can speedup training process when you do training many times. You can disable it by setting ```use_buffer=0```.
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If ```agaricus.txt.test.buffer``` exists, and automatically loads from binary buffer if possible, this can speedup training process when you do training many times. You can disable it by setting ```use_buffer=0```.
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- Buffer file can also be used as standalone input, i.e if buffer file exists, but original agaricus.txt.test was removed, xgboost will still run
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* Deviation from LibSVM input format: xgboost is compatible with LibSVM format, with the following minor differences:
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- xgboost allows feature index starts from 0
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