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NEW QUESTION: 1
Which method would you supply to a class implementing the Callable interface?
A. executable ()
B. start ()
C. callable ()
D. call ()
E. run ()
Answer: D
Explanation:
Explanation/Reference:
Explanation:
public interface Callable<V>
A task that returns a result and may throw an exception. Implementors define a single method with no
arguments called call.
Note:
Interface Callable<V>
Type Parameters:
V - the result type of method call
The Callable interface is similar to Runnable, in that both are designed for classes whose instances are
potentially executed by another thread. A Runnable, however, does not return a result and cannot throw a
checked exception.
The Executors class contains utility methods to convert from other common forms to Callable classes.
Reference:java.util.concurrent
NEW QUESTION: 2
Überprüfen Sie, welche der folgenden Optionen den GRÖSSTEN Input für den Asset-Klassifizierungsprozess liefern würde.
A. Wiederbeschaffungskosten des Vermögenswerts
B. Konformitätsanforderungen
C. Empfindlichkeit der Daten
D. Risikobewertung
Answer: C
NEW QUESTION: 3
償却領域は何を制御しますか？
この質問には2つの正解があります
応答：
A. アセットクラス
B. 推定耐用年数
C. 減価償却の開始日
D. 減価償却勘定
Answer: B,C
NEW QUESTION: 4
Azure Machine Learningワークスペースには、real_estate_dataという名前のデータセットがあります。データセット内のデータのサンプルを次に示します。
自動機械学習を使用して、価格列を予測するための最適な回帰モデルを見つけたいと考えています。
Azure Machine Learning SDKを使用して、自動機械学習実験を構成する必要があります。
コードをどのように完成させる必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。
Answer:
Explanation:
Explanation:
Box 1: training_data
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). If training_data is specified, then the label_column_name parameter must also be specified.
Box 2: validation_data
Provide validation data: In this case, you can either start with a single data file and split it into training and validation sets or you can provide a separate data file for the validation set. Either way, the validation_data parameter in your AutoMLConfig object assigns which data to use as your validation set.
Example, the following code example explicitly defines which portion of the provided data in dataset to use for training and validation.
dataset = Dataset.Tabular.from_delimited_files(data)
training_data, validation_data = dataset.random_split(percentage=0.8, seed=1) automl_config = AutoMLConfig(compute_target = aml_remote_compute, task = 'classification', primary_metric = 'AUC_weighted', training_data = training_data, validation_data = validation_data, label_column_name = 'Class' ) Box 3: label_column_name label_column_name:
The name of the label column. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.
This parameter is applicable to training_data and validation_data parameters.
Incorrect Answers:
X: The training features to use when fitting pipelines during an experiment. This setting is being deprecated. Please use training_data and label_column_name instead.
Y: The training labels to use when fitting pipelines during an experiment. This is the value your model will predict. This setting is being deprecated. Please use training_data and label_column_name instead.
X_valid: Validation features to use when fitting pipelines during an experiment.
If specified, then y_valid or sample_weight_valid must also be specified.
Y_valid: Validation labels to use when fitting pipelines during an experiment.
Both X_valid and y_valid must be specified together.
exclude_nan_labels: Whether to exclude rows with NaN values in the label. The default is True.
y_max: y_max (float)
Maximum value of y for a regression experiment. The combination of y_min and y_max are used to normalize test set metrics based on the input data range. If not specified, the maximum value is inferred from the data.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py