00:01
Okay, so in this question, there are two different questions.
00:06
So let's go ahead and start with the first one.
00:09
And we're going to know what does high variance indicate.
00:13
But first, let's talk about what is high variance.
00:26
High variance is a, okay, so a model with high variance may represent the data set accurately.
00:34
So represents data accurate.
00:45
But could lead to overfitting, too noisy, or otherwise unrepresentative training data.
01:25
Well, that definition is kind of really hard to understand, but a model with high bias may underfit the training data due to a simpler model that overlooks regularities in the data.
01:39
So let's go through the answer choices.
01:42
So a states that the mean is very high.
01:49
So any of the ones that have to do with mean are untrue.
01:56
So you can go ahead.
01:57
So we have a, b, c, d, and e.
02:06
So you can mark off a because it mentions mean.
02:09
Mean has nothing to do with the high variance.
02:13
B also mentions mean, c also mentions mean, d also mentions mean, so that means for that one, your answer would be e because e says the variation among the values is high.
02:30
So values variation is high.
02:40
So the second question asks about central assumptions and the multifactoral and hairl...
