In this article, you will find **Coursera machine learning week 3 Quiz answer Logistic Regression | Andrew NG**. Use “Ctrl+F” To Find Any Questions or Answers. For Mobile Users, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Options to Get Any Random Questions Answer.

Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. Don’t just copy-paste the code for the sake of completion. Even if you copy the code, make sure you understand the code first.

**Coursera machine learning week 3 Quiz answer Logistic Regression | Andrew NG**

1. Suppose that you have trained a logistic regression classifier, and it outputs on a new example a prediction = 0.2. This means (check all that apply):

- Our estimate for P(y = 1|x; θ) is 0.8.
- Our estimate for P(y = 0|x; θ) is 0.8.
Since we must have P(y=0|x;θ) = 1 – P(y=1|x; θ), the former is

1 – 0.2 = 0.8. - Our estimate for P(y = 1|x; θ) is 0.2.
h(x) is precisely P(y=1|x; θ), so each is 0.2.

- Our estimate for P(y = 0|x; θ) is 0.2.
h(x) is P(y=1|x; θ), not P(y=0|x; θ)

- Our estimate for P(y = 1|x; θ) is 0.8.

2. Suppose you have the following training set, and fit a logistic regression classifier .

**Which of the following are true? Check all that apply.**

- Adding polynomial features (e.g., instead using ) could increase how well we can fit the training data.
- At the optimal value of θ (e.g., found by fminunc), we will have J(θ) ≥ 0.
- Adding polynomial features (e.g., instead using ) would increase J(θ) because we are now summing over more terms.
- If we train gradient descent for enough iterations, for some examples in the training set it is possible to obtain . Which of these is a correct gradient descent update for logistic regression with a learning rate of ? Check all that apply.
4. Which of the following statements are true? Check all that apply.

- The one-vs-all technique allows you to use logistic regression for problems in which each comes from a fixed, discrete set of values.
If each is one of k different values, we can give a label to each and use one-vs-all as described in the lecture.

- For logistic regression, sometimes gradient descent will converge to a local minimum (and fail to find the global minimum). This is the reason we prefer more advanced optimization algorithms such as fminunc (conjugate gradient/BFGS/L-BFGS/etc).
The cost function for logistic regression is convex, so gradient descent will always converge to the global minimum. We still might use a more advanced optimisation algorithm since they can be faster and don’t require you to select a learning rate.

- The cost function for logistic regression trained with examples is always greater than or equal to zero.
The cost for any example is always since it is the negative log of a quantity less than one. The cost function is a summation over the cost for each sample, so the cost function itself must be greater than or equal to zero.

- Since we train one classifier when there are two classes, we train two classifiers when there are three classes (and we do one-vs-all classification).
We will need 3 classfiers. One-for-each class.

- The one-vs-all technique allows you to use logistic regression for problems in which each comes from a fixed, discrete set of values.

5. Suppose you train a logistic classifier . Suppose , , . Which of the following figures represents the decision boundary found by your classifier?

- Figure:

In this figure, we transition from negative to positive when x1 goes from left of 6 to right of 6 which is true for the given values of θ.

- Figure:

- Figure:

- Figure:

**Disclaimer:**Hopefully, this article will be useful for you to find all the**Coursera machine learning week 3 Quiz answer Logistic Regression | Andrew NG**

Finally, we are now, in the end, I just want to conclude some important message for you, Feel free to ask doubts in the comment section. I will try my best to answer it. If you find this helpful by any means like, comment, and share the post. Please share our posts on social media platforms and also suggest to your friends to Join Our Groups. Don’t forget to subscribe. This is the simplest way to encourage me to keep doing such work.

**Is Andrew Ng’s Machine Learning course good?**

It is the Best Course for Supervised Machine Learning! Andrew Ng Sir has been like always has such important & difficult concepts of Supervised ML with such ease and great examples, Just amazing!

**How do I get answers to coursera assignment?**

Use “Ctrl+F” To Find Any Questions Answered. & For Mobile Users, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Options to Get Any Random Questions Answer.

**How long does it take to finish coursera Machine Learning?**

this specialization requires approximately**3 months**with 75 hours of materials to complete, and I finished it in 3 weeks and spent an additional 1 week reviewing the whole course.

**How do you submit assignments on Coursera Machine Learning?**

Submit a programming assignment**Open the assignment page for the assignment you want to submit**. Read the assignment instructions and download any starter files. Finish the coding tasks in your local coding environment. Check the starter files and instructions when you need to. Reference