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big-data

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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.

  • Updated Feb 18, 2021
  • Python
pseudotensor
pseudotensor commented Jan 12, 2021

Problem: the approximate method can still be slow for many trees
catboost version: master
Operating System: ubuntu 18.04
CPU: i9
GPU: RTX2080

Would be good to be able to specify how many trees to use for shapley. The model.predict and prediction_type versions allow this. lgbm/xgb allow this.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

  • Updated May 4, 2021
  • Jupyter Notebook
vespa
kkraune
kkraune commented Apr 2, 2021

... to make it easier to read Vespa documentation on an e-reader / offline

Vespa documentation is generated using Jekyll from .md and .html files, look into options for generating the artifact as part of site generation (there might be plugins we can use here)

proddata
proddata commented Apr 23, 2021

Use case:
Right now one can only use date_trunc() to easily define time buckets. date_trunc() only supports predefine time intervals like 1 minute, 1 hour, etc. . In time-series use cases it is often necessary to define different time bucket sizes like e.g. '5 minutes' or '20 minutes'

a workaround for this is the - error prone - integer division on the timestamp e.g.

S

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