For this question, assume that you have 1 compound. This compound is divided in half once, so you are left with 0.5. That 0.5 that remains is divided in half again, this is the second half-life, and you are left with 0.25. The final half life involves dividing 0.25 in half, which means you are left with 0.125. For the answer to make sense, you need to know your conversions between decimals and fractions. To make it simple, if you have 0.125 and you times it by 8, you are left with your initial value of 1. Therefore, after three half-lives, you are left with 1/8th of the compound.
Answer:
Beryllium, because it is in period 2 and has four total electrons.
Explanation:
Answer:
Ethane would have a higher boiling point.
Explanation:
In this case, for the lewis structures, we have to keep in mind that all atoms must have <u>8 electrons</u> (except hydrogen). Additionally, each carbon would have <u>4 valence electrons</u>, with this in mind, for methane we have to put the hydrogens around the carbon, and with this structure, we will have 8 electrons for the carbon. In ethane, we will have a bond between the carbons, therefore we have to put three hydrogens around each carbon to obtain 8 electrons for each carbon.
Now, the main difference between methane and ethane is an <u>additional carbon</u>. In ethane, we have an additional carbon, therefore due to this additional carbon, we will have <u>more area of interaction</u> for ethane. If we have more area of interaction we have to give <u>more energy</u> to the molecule to convert from liquid to gas, so, the ethane will have a higher boiling point.
I hope it helps!
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.
- Entropy is actually used when you use information gain.
Learn more about entropy here,
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To be honest answer might be a
