# ▸ Linear Regression with Multiple Variables :

Recommended Machine Learning Courses:

1. Suppose m=4 students have taken some classes, and the class had a midterm exam and a final exam. You have collected a dataset of their scores on the two exams, which is as follows:

You’d like to use polynomial regression to predict a student’s final exam score from their midterm exam score. Concretely, suppose you want to fit a model of the form $\inline&space;h_{\theta}(x)&space;=&space;\theta_{0}&space;+&space;\theta_{1}&space;x_{1}&space;+&space;\theta_{2}&space;x_{2}$, where $\inline&space;x_1$ is the midterm score and x_2 is (midterm score)^2. Further, you plan to use both feature scaling (dividing by the “max-min”, or range, of a feature) and mean normalization.
What is the normalized feature $\inline&space;x_2^{(4)}$ ? (Hint: midterm = 69, final = 78 is training example 4.) Please round off your answer to two decimal places and enter in the text box below.
2. -0.47

1. You run gradient descent for 15 iterations with $\inline&space;\alpha&space;=&space;0.3$ and compute after each iteration. You find that the value of $\inline&space;J(\theta)$ decreases slowly and is still decreasing after 15 iterations. Based on this, which of the following conclusions seems most plausible?

1. You run gradient descent for 15 iterations with $\inline&space;\alpha&space;=&space;0.3$ and compute after each iteration. You find that the value of $\inline&space;J(\theta)$ decreases quickly then levels off. Based on this, which of the following conclusions seems most plausible?

1. Suppose you have m = 23 training examples with n = 5 features (excluding the additional all-ones feature for the intercept term, which you should add). The normal equation is $\inline&space;\theta&space;=&space;(X^{T}&space;X)^{-1}X^{T}y$. For the given values of m and n, what are the dimensions of $\inline&space;\theta$, X, and y in this equation?
• X is 23 × 5, y is 23 × 1, θ is 5 × 1

1. Suppose you have a dataset with m = 1000000 examples and n = 200000 features for each example. You want to use multivariate linear regression to fit the parameters $\inline&space;\theta$ to our data. Should you prefer gradient descent or the normal equation?
• Gradient descent, since it will always converge to the optimal θ.
• Gradient descent, since $\inline&space;(X^T&space;X)^{-1}$ will be very slow to compute in the normal equation.
• With n = 200000 features, you will have to invert a 200001 x 200001 matrix to compute the normal equation. Inverting such a large matrix is computationally expensive, so gradient descent is a good choice.
• The normal equation, since it provides an efficient way to directly find the solution.
• The normal equation, since gradient descent might be unable to find the optimal θ.

### Check-out our free tutorials on IOT (Internet of Things):

1. Which of the following are reasons for using feature scaling?
• It speeds up solving for θ using the normal equation.
• The magnitute of the feature values are nsignificant in terms of computational cost.
• It speeds up gradient descent by making it require fewer iterations to get to a good solution.
• Feature scaling speeds up gradient descent by avoiding many extra iterations that are required when one or more features takes on much larger values than he rest.

&
Click here to see more codes for Raspberry Pi 3 and similar Family.
&
Click here to see more codes for NodeMCU ESP8266 and similar Family.
&
Click here to see more codes for Arduino Mega (ATMega 2560) and similar Family.

Feel free to ask doubts in the comment section. I will try my best to answer it.
If you find this helpful by any mean like, comment and share the post.
This is the simplest way to encourage me to keep doing such work.

Thanks & Regards,
- APDaga DumpBox

1. can you please show how you got the answer to question1

2. can you please show how you got the answer to question1

3. I tried to solve Question 1:
I used mean Normalization:
(4761 - mean)/standard deviation
and I got answer = -0.95

Can you tell me what I am doing wrong?

1. As per my calculations,
ANS = (4761 - mean) / (max - min)

means = avg = (7921 + 5184 + 8836 + 4761)/4 = 6675.7
(max - min) = 8836 - 4761 = 4075

So, ANS = (4761 - 6675.5) / 4075 = -0.4698 = -0.47

2. OK thanks.I got it.
I was dividing by standard deviation.

3. why did you use 4761 in formulae

4. In the question itself it asked normalization feature for x_2^{(4)} (i.e. 4th entry for x_2)
Please check the table given in the question. x_2^{(4)} = 4761

4. Here, two techniques to perform Feature Scaling are mentioned:

https://www.geeksforgeeks.org/ml-feature-scaling-part-2/

But why following formula is mentioned in Coursera course and in question 1 also?

(x - mean) / {max - min)