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Kaggle 技术秘籍.pdf

发布时间：2022-05-31 发布人：admin 分类：说明书 资料大小：2.46M 资料格式：pdf 举报 版权申诉
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    Winning Kaggle Competitions Hendrik Jacob van Veen - Nubank Brasil
    About Kaggle Biggest platform for competitive data science in the world Currently 500k + competitors Great platform to learn about the latest techniques and avoiding overfit Great platform to share and meet up with other data freaks
    Approach Get a good score as fast as possible Using versatile libraries Model ensembling
    Get a good score as fast as possible Get the raw data into a universal format like SVMlight or Numpy arrays. Failing fast and failing often / Agile sprint / Iteration Sub-linear debugging: 
 “output enough intermediate information as a calculation is progressing to determine before it finishes whether you've injected a major defect or a significant improvement.” Paul Mineiro
    Using versatile libraries Scikit-learn Vowpal Wabbit XGBoost Keras Other tools get Scikit-learn API wrappers
    Model Ensembling Voting Averaging Bagging Boosting Binning Blending Stacking
    General Strategy Try to create “machine learning”-learning algorithms with optimized pipelines that are: Data agnostic (Sparse, dense, missing values, larger than memory) Problem agnostic (Classification, regression, clustering) Solution agnostic (Production-ready, PoC, latency) Automated (Turn on and go to bed) Memory-friendly (Don’t want to pay for AWS) Robust (Good generalization, concept drift, consistent)
    First Overview I Classification? Regression? Evaluation Metric Description Benchmark code “Predict human activities based on their smartphone usage. Predict if a user is sitting, walking etc.” - Smartphone User Activity Prediction Given the HTML of ~337k websites served to users of StumbleUpon, identify the paid content disguised as real content. - Dato Truly Native?
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