researchableShap （646个子文件） 
logo.ai 248KB shap_logo_scratch.ai 218KB make.bat 8KB shap_nips.bib 499B nature_bme.bib 495B tree_explainer.bib 464B treeshap_arxiv.bib 372B _cext.cc 25KB _cext_gpu.cc 8KB setup.cfg 31B run_with_env.cmd 3KB bootstrap.min.css 98KB style.css 1KB style.css 977B NHANESI_X.csv 5.52MB tweets.csv 3.26MB NHANESI_subset_X.csv 1.27MB loan_data.csv 734KB NHANESI_y.csv 286KB NHANESI_subset_y.csv 227KB trial_data2_3_3_2019.csv 139KB trial_data_3_3_2019.csv 57KB trialdata.csv 8KB eventdata.csv 461B imagenet50_labels.csv 259B questiondata.csv 0B _cext_gpu.cu 14KB adult.data 3.79MB participants.db 32KB .DS_Store 6KB .DS_Store 6KB .DS_Store 6KB glyphicons-halflings-regular.eot 20KB
shap_researchable-0.0.1.tar.gz 356KB gpu_treeshap.h 60KB tree_shap.h 57KB tree_shap.h 57KB
League of Legends Win Prediction with XGBoost.html 4.18MB Census income classification with XGBoost.html 3.96MB Census income classification with LightGBM.html 3.67MB decision_plot.html 2.59MB dependence_plot.html 2.41MB Census income classification with LightGBM.html 2.25MB Census income classification with scikit-learn.html 2.18MB Explain an Intermediate Layer of VGG16 on ImageNet.html 1.92MB NHANES I Survival Model.html 1.62MB Sentiment Analysis with Logistic Regression.html 1.25MB Explain an Intermediate Layer of VGG16 on ImageNet (PyTorch).html 971KB Iris classification with scikit-learn.html 699KB Census income classification with Keras.html 668KB Keras LSTM for IMDB Sentiment Classification.html 643KB ImageNet VGG16 Model with Keras.html 553KB Front Page DeepExplainer MNIST Example.html 414KB PyTorch Deep Explainer MNIST example.html 355KB ad.html 4KB exp.html 3KB consent.html 3KB error.html 2KB index.html 2KB thanks-mturksubmit.html 2KB default.html 1KB thanks.html 836B complete.html 814B closepopup.html 469B layout.html 145B favicon.ico 4KB favicon.ico 1KB MANIFEST.in 23B pytest.ini 77B Image Captioning using Azure Cognitive Services.ipynb 7.22MB League of Legends Win Prediction with XGBoost.ipynb 4MB Census income classification with XGBoost.ipynb 3.83MB Figures 8-11 NHANES I Survival Model-Copy1.ipynb 3.43MB Abstractive Summarization Explanation Demo.ipynb 3.08MB decision_plot.ipynb 2.28MB Image Captioning using Open Source.ipynb 2.19MB Figures 8-11 NHANES I Survival Model.ipynb 2.18MB An introduction to explainable AI with Shapley values.ipynb 2.17MB Census income classification with LightGBM.ipynb 2.09MB Figure 4 - Supervised Clustering Adult Census Data.ipynb 1.93MB Sentiment Analysis with Logistic Regression.ipynb 1.89MB Explain an Intermediate Layer of VGG16 on ImageNet.ipynb 1.7MB scatter.ipynb 1.65MB Fitting a Linear Simulation with XGBoost.ipynb 1.62MB NHANES I Survival Model.ipynb 1.36MB Front page example (XGBoost).ipynb 1.28MB Diabetes regression.ipynb 1.23MB bar.ipynb 1.12MB bar.ipynb 1.12MB Machine Translation Explanation Demo.ipynb 1.12MB Analyse Results.ipynb 1.07MB Catboost tutorial.ipynb 1.02MB Figure 7 - Airline Tweet Sentiment Analysis.ipynb 972KB Emotion Data Multiclass Text Explanation Demo.ipynb 972KB text.ipynb 960KB Open Ended Text Generation Explanation Demo.ipynb 866KB Explain an Intermediate Layer of VGG16 on ImageNet (PyTorch).ipynb 720KB Language Modelling Explanation Demo.ipynb 712KB beeswarm.ipynb 700KB Image Multi Class.ipynb 674KB共 646 条
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