Which of the Following Can Be Processed Using the Afis?

▸ Unsupervised Learning :

1. For which of the following tasks might Chiliad-means clustering exist a suitable algorithm
   Select all that utilise.
   
   
   • Given a set of news articles from many dissimilar news websites, find out what are the main topics covered.

     Grand-ways tin can cluster the articles and and then we tin can inspect them or use other methods to infer what topic each cluster represents
     

   
   
   • Given historical weather records, predict if tomorrow'south weather will be sunny or rainy.
   
   
   • From the user usage patterns on a website, effigy out what different groups of users be.

     We tin can cluster the users with Thou-means to find unlike, distinct groups.

   
   
   • Given many emails, you want to determine if they are Spam or Not-Spam emails.
   
   
   • Given a database of data about your users, automatically group them into different market segments.

     Y'all can use K-means to cluster the database entries, and each cluster will correspond to a different market segment.

   
   
   • Given sales information from a big number of products in a supermarket, figure out which products tend to form coherent groups (say are frequently purchased together) and thus should exist put on the same shelf.

     If you cluster the sales information with M-ways, each cluster should correspond to coherent groups of items.

   
   
   • Given sales data from a large number of products in a supermarket, judge future sales for each of these products.
   
   

---

2. Suppose we accept three cluster centroids , and .
   Furthermore, we have a training example . After a cluster assignment
   step, what will exist?

---

3. Yard-means is an iterative algorithm, and two of the following steps are repeatedly carried out in its inner-loop. Which ii?

---

4. Suppose yous have an unlabeled dataset . You run Thousand-ways with l unlike random initializations, and obtain fifty different clusterings of the data.

   What is the recommended mode for choosing which i of these 50 clusterings to apply?

---

Bank check-out our free tutorials on IOT (Cyberspace of Things):

---

5. Which of the following statements are true? Select all that apply.
   
   
   • On every iteration of Thou-means, the cost function (the distortion function) should either stay the same or decrease; in particular, it should not increase.

     Both the cluster assignment and cluster update steps decrese the cost / distortion function, so it should never increase subsequently an iteration of Yard-means.

   
   
   • A good way to initialize Thousand-means is to select K (distinct) examples from the training set up and set the cluster centroids equal to these selected examples.

     This is the recommended method of initialization.

   
   
   • G-Means will always give the same results regardless of the initialization of the centroids.
   
   
   • Once an case has been assigned to a detail centroid, it volition never be reassigned to another different centroid
   
   
   • For some datasets, the "right" or "correct" value of K (the number of clusters) can exist ambiguous, and difficult even for a human adept looking carefully at the data to decide.

     In many datasets, different choices of K will give different clusterings which announced quite reasonable. With no labels on the data, we cannot say one is better than the other.

   
   
   • The standard style of initializing K-ways is setting to be equal to a vector of zeros.
   
   
   • If we are worried about M-ways getting stuck in bad local optima, one way to amend (reduce) this trouble is if we try using multiple random initializations.

     Since each run of 1000-means is independent, multiple runs can observe unlike optima, and some should avoid bad local optima.

   
   
   • Since Yard-Means is an unsupervised learning algorithm, it cannot overfit the data, and thus it is e'er better to have as large a number of clusters every bit is computationally feasible.
   
   

---

Click hither to run across solutions for all Machine Learning Coursera Assignments.
&
Click hither to see more codes for Raspberry Pi three and similar Family.
&
Click here to come across more codes for NodeMCU ESP8266 and similar Family unit.
&
Click here to run across more than codes for Arduino Mega (ATMega 2560) and similar Family.

Experience costless to ask doubts in the comment department. I will endeavour my best to answer it.
If you lot 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.

Cheers & Regards,
- APDaga DumpBox

dukenour1960.blogspot.com

Source: https://www.apdaga.com/2019/11/coursera-machine-learning-week-8-quiz-unsupervised-learning.html

Share this post