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NEW QUESTION: 1
A customer contacts you for assistance to expand an HPE Synergy environment.
The existing environment contains:
* one Synergy 12000 frame
* two Composers
* eight SY480 Compute Modules -1 CPU - 384GB RAM - 3820C 10/20GbE CNA
* one D3940 Storage Module
The new requirement Is 16 Gb FC connectivity to a newly-deployed 3PAR storage array Which components should you include? (Select two.)
A. an additional Virtual Connect SE 40 Gb F8
B. Synergy 3830C Host Bus Adapter
C. a second CPU per Compute Module
D. two QSFP+ AOC/DAC cables for ICM clustering
E. HPE Synergy FC License Upgrade per Composer pair
Answer: C,D

NEW QUESTION: 2
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B. Specify the cluster node autodiscovery to True in Order to enable the cluster ID autodiscovery.
C. Specify the cluster settings of each cluster node in the %HYBRIS_BIN%/config/tomcat/conf/server.xml file
D. Specify exactly the same cluster settings for each node in the %HYBRIS_BIN%/config/local properties file
Answer: A,C

NEW QUESTION: 3
決定木アルゴリズムを使用しています。次のツリー深度で一般化するモデルを訓練しました。
10。
さまざまなツリー深度値を持つモデルのバイアスおよび分散プロパティを選択する必要があります。
各ツリーの深さに対してどのプロパティを選択する必要がありますか?回答するには、回答エリアで適切なオプションを選択します。

Answer:
Explanation:

Explanation

In decision trees, the depth of the tree determines the variance. A complicated decision tree (e.g. deep) has low bias and high variance.
Note: In statistics and machine learning, the bias-variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa. Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.
References:
https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/

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