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Paladinen [302]
3 years ago
15

Put the sausage in the doughnut

Chemistry
1 answer:
svetoff [14.1K]3 years ago
7 0

Answer:

I already did.

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Which of the following does NOT play a role in South Florida’s climate?
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i think a im not sure

Explanation:

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A student places one piece of chalk into a bowl with vinegar and another into a bowl with water. The next day she observes that
USPshnik [31]

Heavy rainfall causes a landslide. Hence, option A is correct.

<h3>What is a landslide?</h3>

A landslide is defined as the movement of a mass of rock, debris, or earth down a slope.

Water can trigger landslides and mudslides because it alters the pressure within the slope, which leads to slope instability.

Consequently, the heavy water-laden slope materials (soil, rock, etc.) will succumb to the forces of gravity.

Hence, option A is correct.

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2 years ago
2. What effect does adding a neutron have on the atom’s mass?
anyanavicka [17]
It does<span>, however, change the </span>mass<span> of the nucleus. </span>Adding<span> or removing </span>neutrons<span>from the nucleus are how isotopes are created. Protons carry a positive electrical charge and they alone determine the charge of the nucleus.</span>
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3 years ago
Entropy A. has a strong odor B. is a measure of correlation between numeric variables C. is used to calculate information gain D
Bingel [31]

Correct option:

Entropy is used to calculate information gain.

What is entropy?

  • Entropy is the measure of data's uncertainty or randomness; the greater the randomness, the higher the entropy. Entropy is used by information gain to make choices. Information will increase if the entropy decreases.
  • Decision trees and random forests leverage information gained to determine the appropriate split. Therefore, the split will be better and the entropy will lower the more information gained.
  • Information gain is calculated by comparing the entropy of a dataset before and after a split.
  • Entropy is a way to quantify data uncertainty. The goal is to maximize information gain while minimizing entropy. The method prioritizes the feature with the most information, which is utilized to train the model.
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