How to identify the normalized feature

I have been trying to solve a problem stated in an exam of coursera. I am not seeking the solution but I need to get the steps and concepts to resolve this. Can any one share the concept and steps to help me find the solution.

UPDATE: I was expecting a down-vote and its not unusual, as its the most easiest thing people can do. I am seeking the direction to solve the problem as I wasn't able to get the idea to solve it after watching the videos on Coursera. I hope someone sensible out there can share a direction and step to achieve the mentioned goal.

3 Answers

Answers 1

The first step of solving this question is to identify what is , from the content of the lecture, it refers to the first feature of the third training case. Which is the unsquared version of the midterm score in the third row of the table.

Secondly, you need to understand the concept of normalization. The reason why we need normalization is that the value of some features among all training examples may much larger than the value of other features, which may make the cost function have pretty bad shape and this will make it harder gradient descent to find the minimum. In order to solve this, we want to make all features have nearly the same scale, and make the range of the feature to be centered at zero.

In this question, we want to scale every feature to a scale of 1, in order to do this, you need to find the max and min value of the feature among all training cases. Then squeeze the range of the feature to 0 and 1. The second step is to find the center value of the feature (average value in this case) and move the center value of the feature to 0.

I think this is pretty much all hints I can give you, you will totally be able to calculate the answer to this question by yourself from this point.

Answers 2

Your course would have covered on the mean normaliazation and min-max scaling as:xn = xt -μ /(xmax - xmin) .

Applying it on the above table would lead to:

Now the tricky part of the question is: you need to figure out whether the 3rd value of mindterm exam square is either (0.530184) or is it the square of (0.52): the midterm value.

Note: Look at the function you want to fit.

Answers 3

Mean Normalization

Mean normalization, also known as 'standardization', is one of the most popular techniques of feature scaling.

Andrew Ng describes it in the 12a slide of lecture 4:

How to resolve the problem

The problem asks you to standardize the first feature in the third example: midterm = 94;
well, we have just to resolve the equation!

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Just for clarity, the notation:

μ (mu) = "avg value of x in training set", in other words: the mean of the x1 column.

σ (sigma) = "range (max-min)", literaly σ = max-min (of the x1 column).

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So:

μ = ( 89 + 72 + 94 +69 )/4 = 81

σ = ( 94 - 69 ) = 25

x_std = (94 - 81)/25 = 0.52

Result: 0.52

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Best regards, Marco.

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