試験Databricks-Machine-Learning-Associate トピック2 問題5 スレッド
Databricks Databricks-Machine-Learning-Associateのリアル試験問題集
問題 #: 5
トピック #: 2
問題 #: 5
トピック #: 2
A data scientist wants to use Spark ML to impute missing values in their PySpark DataFrame features_df. They want to replace missing values in all numeric columns in features_df with each respective numeric column's median value.
They have developed the following code block to accomplish this task:
The code block is not accomplishing the task.
Which reasons describes why the code block is not accomplishing the imputation task?
They have developed the following code block to accomplish this task:
The code block is not accomplishing the task.
Which reasons describes why the code block is not accomplishing the imputation task?
おすすめの解答：D 解答を投票する
In the provided code block, the Imputer object is created but not fitted on the data to generate an ImputerModel. The transform method is being called directly on the Imputer object, which does not yet contain the fitted median values needed for imputation. The correct approach is to fit the imputer on the dataset first.
Corrected code:
imputer = Imputer( strategy="median", inputCols=input_columns, outputCols=output_columns ) imputer_model = imputer.fit(features_df) # Fit the imputer to the data imputed_features_df = imputer_model.transform(features_df) # Transform the data using the fitted imputer Reference:
PySpark ML Documentation
Corrected code:
imputer = Imputer( strategy="median", inputCols=input_columns, outputCols=output_columns ) imputer_model = imputer.fit(features_df) # Fit the imputer to the data imputed_features_df = imputer_model.transform(features_df) # Transform the data using the fitted imputer Reference:
PySpark ML Documentation
Tokuzawa 2026-07-19 08:59:53
コメント
他人の解答コメントを賛成するのも、その解答に一票を入れることになります。したがって、すでに同じ意見の投票コメントが存在する場合、新規コメントをする代わりに賛成することもできます。
コメントを通報する
コメント中
今すぐ 新規登録 / ログイン （無料です）。