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...
You need to choose the runtime for ML instead of the standard.
executing the following code...from databricks import automlsummary = automl.regress(train_df, target_col="price", primary_metric="rmse", timeout_minutes=5, max_trials=10)generates the error...ImportError: cannot import name 'automl' from 'databricks...
I'm happy to see a particularly subject.
the following code...from pyspark.sql.functions import monotonically_increasing_id, lit, expr, randimport uuidfrom databricks import feature_storefrom pyspark.sql.types import StringType, DoubleTypefrom databricks.feature_store import feature_table, ...
Hope that was an easy fix - @Tobias Cortese ! Thanks for marking the "best answer"!
