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...
AI代码解释 # 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...
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...
用一个相对简单或者使用不同的模型(meta-model),比如说lasso,将新的训练进行拟合,然后将拟合后的模型预测新的测试集new_test_dataset,得到新的模型 将新的模型去拟合新的测试集(new_test_dataset),得到预测的结果models = [KernelRidge(),ElasticNet(),Lasso(),GradientBoostingRegressor(),BayesianRidge(),LassoL...
(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,...
Adam 1e-4 (no scheduler) Epoch: 15 Ensemble: EMA (after 6 epoch) https://github.com/ajbrock/BigGAN-PyTorch Batch: 3 per GPU in 3 1080Ti on 576px (no accumulation) (2 per GPU on 704px) Classification: None (hard to select thresholds on the private dataset) Threshold: 0.5 (no sea...
(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...
Jason: What classical methods to chess rating did you apply to the dataset, if any, and when over the course of the competition? I used none. My idea was always to get a normalized rating between 0.0 and 1.0 for every player. Jason: You have released a detailed descriptio...
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?
if I asked you to predict the top speed of a car from its horsepower and other characteristics, and gave you a dataset that randomly had cars with very high top speeds, you would create a model that overstated speed. The way to figure out if your model is doing this is to evaluate it...