<p>The question is quite old, my previous answer about xgboost seems oudated given the latest developments of <a href="https://github.com/Microsoft/LightGBM/" rel="nofollow noreferrer">LightGBM</a> implementing various tree based learning algorithms :</p>

<ul>
<li><a href="https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf" rel="nofollow noreferrer">GBDT</a>, Gradient boosting decision tree</li>
<li><a href="https://arxiv.org/pdf/1505.01866.pdf" rel="nofollow noreferrer">DART</a>, or Dropouts meet Multiple Additive Regression Trees</li>
<li>GOSS, or Gradient-based One-Side Sampling</li>
<li>Random Forest</li>
</ul>

<p>It also has a <a href="https://lightgbm.readthedocs.io/en/latest/Python-API.html" rel="nofollow noreferrer">Python API</a>.</p>
Library for gradient boosting tree
<p>Do you know of a good library for gradient boosting tree machine learning?</p>

<p>preferably:</p>

<ul>
<li>with good algorithms such as AdaBoost, TreeBoost, AnyBoost, LogitBoost, etc</li>
<li>with configurable weak classifiers</li>
<li>capable of both classification and prediction (regression)</li>
<li>with all kinds of allowed signals: numbers, categories or free text</li>
<li>C/C++ or Python</li>
<li>opensource</li>
</ul>

<p>So far I have found <a href="http://www.multiboost.org/home" rel="noreferrer">http://www.multiboost.org/home</a> which looks good. But I wonder if there are other libraries?</p>