usage: kaggle datasets [-h]{list,files,download,create,version,init,metadata,status} ...optional arguments:-h, --help show this help message and exitcommands:{list,files,download,create,version,init,metadata, status}list List available datasetsfiles List dataset filesdownload Download dataset files...
kaggle datasets download-dcisautomotiveapi/large-car-dataset 1. 2.2.4 初始化元数据文件以创建数据集 usage: kaggle datasets init [-h] [-p FOLDER] optional arguments: -h,--helpshow this help message andexit -pFOLDER,--pathFOLDER Folderforupload, containing data files and a special dataset-meta...
Often, consumers forget that their daily lives involve IoT, for example the connected car is one of the most sophisticated IoT devices and yet, most people would probably not list this as one of their IoT devices. Telecom operators can also provide personalized plans for consumers based on thei...
复制 # Readinthe datasetasa dataframetrain=pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')test=pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')train.shape,test.shape Output[3]: ((1460, 81), (1459, 80)) EDA 目标 数据集中的每一行...
(BLR) model that has been extended including the ELO rating predictor and two random effects due to the hierarchical structure of the dataset.\nThe predictive power of the BLR model and its extensions has been compared with the one of other statistical modelling approaches (Random Forest, Neural...
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This architecture was a part of thewinning solutiuon(1st out of 735 teams) in theCarvana Image Masking Challenge. Citing TernausNet Please cite TernausNet in your publications if it helps your research: @ARTICLE{arXiv:1801.05746, author = {V. Iglovikov and A. Shvets}, title = {Ternaus...
(X_train)) imputed_X_valid = pd.DataFrame(my_imputer.transform(X_valid)) # Imputation removed column names; put them back imputed_X_train.columns = X_train.columns imputed_X_valid.columns = X_valid.columns print("MAE from Approach 2 (Imputation):") print(score_dataset(imputed_X_train,...
Classification: None (hard to select thresholds on the private dataset) Threshold: 0.5 (no search) Pneumothorax Segmentation Models: SE-ResNeXt50(deep enough) Attention: CBAM (CBAM performed better than scSE in many tasks) Loss: Lovazs (tried: combined other losses such as SoftDice with lovasz...
Your dataset had too many variables to wrap your head around(to accept something that one does not particularly want to accept), or even to print out nicely. How can you pare down(减少) this overwhelming amount of data to something you can understand?