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Machine Learning
Dive into the world of machine learning on the Databricks platform. Explore discussions on algorithms, model training, deployment, and more. Connect with ML enthusiasts and experts.
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Slalom_Tobias
by • New Contributor III
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Resolved! ML Practioner | ML 11 - XGBoost notebook | cannot import keras.applications.resnet50

the following code...from sparkdl.xgboost import XgboostRegressorfrom pyspark.ml import Pipelineparams = {"n_estimators": 100, "learning_rate": 0.1, "max_depth": 4, "random_state": 42, "missing": 0}xgboost = XgboostRegressor(**params)pipeline = Pipel...

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Prabakar
Databricks Employee
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You need to choose the runtime for ML instead of the standard.

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Direo
by • Contributor II
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Resolved! xgboost 1.5.1 gives 'XGBModel' object has no attribute 'enable_categorical' error

Should I pip install xgboost==1.4.2. (the last version it worked) or is there a better way to solve it having in mind that this solution might cause problems later if this version of xgboost is not supported on future python versions.

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Direo
Contributor II
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Hi, @Kaniz Fatma​. No, I have found a solution. Needed to retrain models using new version of xgboost.

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