Category : xgboost

I’ve been taking a look at the output of booster.save_model("model.json"), and I am having trouble understanding the output. It seems as though almost none of the information in model.json is actually used for prediction, in fact – suspiciously little. For reference, one such model.json looks like this: j={"learner": { "attributes": {}, "feature_names": [], "feature_types": [], ..

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I have tried uninstalling and reinstalling xgboost. This was working and now doesn’t. I am on jupyter notebook running xgboost v0.90. 20 import xgboost as xgb 21 #XGBRegressor = xgb.XGBRegressor() —> 22 from xgboost import XGBRegressor, plot_importance 23 from sklearn.model_selection import train_test_split, GridSearchCV, KFold, RandomizedSearchCV 24 from sklearn.metrics import mean_squared_error ImportError: cannot import name ‘XGBRegressor’ ..

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As mentioned in question, I keep getting UnexpectedStatusException: Error for HyperParameterTuning job xgboost-211***-1631: Failed. Reason: No training job succeeded after 5 attempts. For additional details, please take a look at the training job failures by listing training jobs for the hyperparameter tuning job. I looked into parameter ranges based on https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost-tuning.html to make sure that ..

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I have the following code, where I use a table with many variables. year = int(i/100) month = i – int(i/100)*100 filtro = [(‘year’,’==’,ano),(‘month’,’==’,mes)] dataset = pq.ParquetDataset(bucket, filesystem=s3, filters=filtro) df = dataset.read_pandas().to_pandas() X = df.loc[:,variable_modelo] y = df[[‘period’]] data_pred = None data_pred = xgb.DMatrix(X, label=y) ypred = model.predict(data_pred) And then I use .predict() of xgb.train(param, ..

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