ngboost
Natural Gradient Boosting for Probabilistic Prediction (by stanfordmlgroup)
data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. (by donnemartin)
ngboost | data-science-ipython-notebooks | |
---|---|---|
1 | 1 | |
1,587 | 26,532 | |
1.0% | - | |
6.7 | 0.0 | |
3 months ago | about 2 months ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ngboost
Posts with mentions or reviews of ngboost.
We have used some of these posts to build our list of alternatives
and similar projects.
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Exploring Estimations and Confidence Intervals of Three Point Shooting
For DARKO, I do this by passing my time-decay/padding/kalman calculations as features (along with other pieces of biographical information) into NGboost. This gives me a point-estimate prediction, as well as confidence intervals. NGboost is somewhat limited in terms of what distributions it can handle, so you need to use care here, but it's very powerful since it can easily work with a large feature space (DARKO uses hundreds of features).
data-science-ipython-notebooks
Posts with mentions or reviews of data-science-ipython-notebooks.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2020-12-27.
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Beginner in Python for Data Science
data science ipython notebooks