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NEW QUESTION: 1
Societal progress usually comes about through (i)_______ and challenge- that is, when people point out
the mistakes of those who wield power; (ii)_______, without our challenging the mistaken notions of
established institutions, political (iii)_______ and tyranny would go unchecked.
A. in fact
B. opposition
C. perseverance
D. nevertheless
E. in addition
F. corruption
G. dissension
H. oppression
I. back
Answer: E,G,H
Explanation:
In the first sentence, the phrase that is signals that what precedes it should be similar in meaning to what
follows it. Pointing out the mistakes of those in power could be characterized either as a challenge or as
part of dissention, which means "rebellion or disobedience." The second part of the sentence (following
the semicolon) reiterates and expresses more pointedly the idea in the first part. The connecting phrase in
fact works well to signal this reiteration. As for blank (iii), oppression (which means "persecution or
tyranny") is the only choice that makes sense in context.

NEW QUESTION: 2
Which two criteria can be used to add and remove a prospect from a dynamic list? Choose 2 answers
A. Email Open for Individual Send
B. Webpage visit
C. Prospect Email status
D. Prospect Score
Answer: C,D

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/