Machine Learning with Python Cognitive Class Exam Answers 2021 100% Correct [Latest Updated ]

by
Machine Learning with Python Cognitive Class Exam Answers 2021

Module 1: Machine Learning

1) Machine Learning uses algorithms that can learn from data without relying on explicitly programmed methods.

  • True
  • False

2)Which are the two types of Supervised learning techniques?

  • Classification and Clustering
  • Classification and K-Means
  • Regression and Clustering
  • Regression and Partitioning
  • Classification and Regression

3)Which of the following statements best describes the Python scikit library?

  • A library for scientific and high-performance computation.
  • A collection of algorithms and tools for machine learning.
  • A popular plotting package that provides 2D plotting as well as 3D plotting.
  • A library that provides high-performance, easy to use data structures.
  • A collection of numerical algorithms and domain-specific toolboxes.

Module 2: Regression

1)Train and Test on the Same Dataset might have a high training accuracy, but its out-of-sample accuracy can be low.

  • True
  • False

2)Which of the following matrices can be used to show the results of model accuracy evaluation or the model’s ability to correctly predict or separate the classes?

  • Confusion matrix
  • Evaluation matrix
  • Accuracy matrix
  • Error matrix
  • Identity matrix

3)When we should use Multiple Linear Regression?

  • When we would like to identify the strength of the effect that the independent variables have on a dependent variable.
  • When there are multiple dependent variables.

Module 3: Classification

1)In K-Nearest Neighbors, which of the following is true:

  • A very high value of K (ex. K = 100) produces an overly generalised model, while a very low value of k (ex. k = 1) produces a highly complex model.
  • A very high value of K (ex. K = 100) produces a model that is better than a very low value of K (ex. K = 1)
  • A very high value of k (ex. k = 100) produces a highly complex model, while a very low value of K (ex. K = 1) produces an overly generalized model.

2)A classifier with lower log loss has better accuracy.

  • True
  • False

3)When building a decision tree, we want to split the nodes in a way that decreases entropy and increases information gain.

  • True
  • False

Module4: Clustering

1)Which one is NOT TRUE about k-means clustering??

  • k-means divides the data into non-overlapping clusters without any cluster-internal structure.
  • The objective of k-means, is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.
  • As k-means is an iterative algorithm, it guarantees that it will always converge to the global optimum.

2)Customer Segmentation is a supervised way of clustering data, based on the similarity of customers to each other.

  • True
  • False

3)How is a center point (centroid) picked for each cluster in k-means?

  • We can randomly choose some observations out of the data set and use these observations as the initial means.
  • We can select the centroid through correlation analysis.

Module 5: Recommender System

1)Collaborative filtering is based on relationships between products and people’s rating patterns.

  • True
  • False

2)Which one is TRUE about Content-based recommendation systems?

  • Content-based recommendation system tries to recommend items to the users based on their profile.
  • In content-based approach, the recommendation process is based on similarity of users.
  • In content-based recommender systems, similarity of users should be measured based on the similarity of the actions of users.

3)Which one is correct about user-based and item-based collaborative filtering?

  • In item-based approach, the recommendation is based on profile of a user that shows interest of the user on specific item
  • In user-based approach, the recommendation is based on users of the same neighborhood, with whom he/she shares common preferences.

Final Exam

Question 1)You can define Jaccard as the size of the intersection divided by the size of the union of two label sets.

  • True
  • False

Question 2) When building a decision tree, we want to split the nodes in a way that increases entropy and decreases information gain.

  • True
  • False

Question 3) Which of the following statements are true? (Select all that apply.)

  • K needs to be initialized in K-Nearest Neighbor.
  • Supervised learning works on labelled data.
  • A high value of K in KNN creates a model that is over-fit
  • KNN takes a bunch of unlabelled points and uses them to predict unknown points.
  • Unsupervised learning works on unlabelled data.

Question 4) To calculate a model’s accuracy using the test set, you pass the test set to your model to predict the class labels, and then compare the predicted values with actual values.

  • True
  • False

Question 5) Which is the definition of entropy?

  • The purity of each node in a decition tree.
  • Information collected that can increase the level of certainty in a particular prediction.
  • The information that is used to randomly select a subset of data.
  • The amount of information disorder in the data.

Question 6) Which of the following is true about hierarchical linkages?

  • Average linkage is the average distance of each point in one cluster to every point in another cluster
  • Complete linkage is the shortest distance between a point in two clusters
  • Centroid linkage is the distance between two randomly generated centroids in two clusters
  • Single linkage is the distance between any points in two clusters

Question 7) The goal of regression is to build a model to accurately predict the continues value of a dependent variable for an unknown case.

  • True
  • False

Question 8) Which of the following statements are true about linear regression? (Select all that apply)

  • With linear regression, you can fit a line through the data.
  • y=a+b_x1 is the equation for a straight line, which can be used to predict the continuous value y.
  • In y=ΞΈ^T.X, ΞΈ is the feature set and X is the β€œweight vector” or β€œconfidences of the equation”, with both of these terms used interchangeably.

Question 9) The Sigmoid function is the main part of logistic regression, where Sigmoid of πœƒ^𝑇.𝑋, gives us the probability of a point belonging to a class, instead of the value of y directly.

  • True
  • False

Question 10) In comparison to supervised learning, unsupervised learning has:

  • Less tests (evaluation approaches)
  • More models
  • A better controlled environment
  • More tests (evaluation approaches), but less models

Question 11) The points that are classified by Density-Based Clustering and do not belong to any cluster, are outliers.

  • True
  • False

Question 12) Which of the following is false about Simple Linear Regression?

  • It does not require tuning parameters
  • It is highly interpretable
  • It is fast
  • It is used for finding outliers

Question 13) Which one of the following statements is the most accurate?

  • Machine Learning is the branch of AI that covers the statistical and learning part of artificial intelligence.
  • Deep Learning is a branch of Artificial Intelligence where computers learn by being explicitely programmed.
  • Artificial Intelligence is a branch of Machine Learning that covers the statistical part of Deep Learning.
  • Artificial Intelligence is the branch of Deep Learning that allows us to create models.

Question 14) Which of the following are types of supervised learning?

  • Classification
  • Regression
  • KNN
  • K-Means
  • Clustering

Question 15) A Bottom-Up version of hierarchical clustering is known as Divisive clustering. It is a more popular method than the Agglomerative method.

  • True
  • False

Question 16) Select all the true statements related to Hierarchical clustering and K-Means.

  • Hierarchical clustering does not require the number of clusters to be specified.
  • Hierarchical clustering always generates different clusters, whereas k-Means returns the same clusters each time it is run.
  • K-Means is more efficient than Hierarchical clustering for large datasets.

Question 17) What is a content-based recommendation system?

  • Content-based recommendation system tries to recommend items to the users based on their profile built upon their preferences and taste.
  • Content-based recommendation system tries to recommend items based on similarity among items.
  • Content-based recommendation system tries to recommend items based on the similarity of users when buying, watching, or enjoying something.

Question 18) Before running Agglomerative clustering, you need to compute a distance/proximity matrix, which is an n by n table of all distances between each data point in each cluster of your dataset.

  • True
  • False

Question 19) Which of the following statements are true about DBSCAN? (Select all that apply)

  • DBSCAN can be used when examining spatial data.
  • DBSCAN can be applied to tasks with arbitrary shaped clusters, or clusters within clusters.
  • DBSCAN is a hierarchical algorithm that finds core and border points.
  • DBSCAN can find any arbitrary shaped cluster without getting affected by noise.

Question 20) In recommender systems, β€œcold start” happens when you have a large dataset of users who have rated only a limited number of items.

  • True
  • False

54 thoughts on “Machine Learning with Python Cognitive Class Exam Answers 2021 100% Correct [Latest Updated ]”

  6. Hi! Quick question that’s entirely off topic. Do you know how to make your site mobile friendly? My site looks weird when viewing from my iphone. I’m trying to find a template or plugin that might be able to fix this problem. If you have any recommendations, please share. Thanks!

    Reply

  7. After examine just a few of the blog posts on your website now, and I really like your manner of blogging. I bookmarked it to my bookmark web site record and shall be checking again soon. Pls try my website online as properly and let me know what you think.

    Reply

  10. What i don’t understood is actually how you are now not actually much more neatly-preferred than you might be now. You’re so intelligent. You already know therefore considerably on the subject of this matter, produced me personally believe it from a lot of numerous angles. Its like men and women don’t seem to be interested unless it?Β¦s something to accomplish with Lady gaga! Your individual stuffs excellent. At all times care for it up!

    Reply

  11. Fantastic blog you have here but I was curious about if you knew of any forums that cover the same topics discussed in this article? I’d really like to be a part of group where I can get suggestions from other experienced individuals that share the same interest. If you have any recommendations, please let me know. Bless you!

    Reply

  12. Today, while I was at work, my sister stole my apple ipad and tested to see if it can survive a 30 foot drop, just so she can be a youtube sensation. My iPad is now destroyed and she has 83 views. I know this is completely off topic but I had to share it with someone!

    Reply

  13. Oh my goodness! a tremendous article dude. Thanks Nevertheless I am experiencing subject with ur rss . Don’t know why Unable to subscribe to it. Is there anybody getting identical rss problem? Anyone who is aware of kindly respond. Thnkx

    Reply

  14. Just want to say your article is as astounding. The clarity in your post is just nice and i could assume you are an expert on this subject. Well with your permission let me to grab your feed to keep up to date with forthcoming post. Thanks a million and please keep up the gratifying work.

    Reply

  16. I’d have to examine with you here. Which is not one thing I usually do! I take pleasure in reading a post that may make folks think. Additionally, thanks for permitting me to comment!

    Reply

  18. I was recommended this blog by my cousin. I’m not sure whether this post is written by him as nobody else know such detailed about my trouble. You are incredible! Thanks!

    Reply

  19. Thanks for another fantastic article. Where else could anybody get that type of information in such an ideal way of writing? I’ve a presentation next week, and I am on the look for such info.

    Reply

  20. I am really impressed with your writing skills and also with the layout on your weblog. Is this a paid theme or did you customize it yourself? Anyway keep up the nice quality writing, it’s rare to see a nice blog like this one these days..

    Reply

  35. This Texas poker app strives to be authentic feeling and looking and it succeeds in this goal. With all-new gameplay that is smoother and more fun than ever before you will be able to play no matter where you are. Zynga Poker also has the function of organizing poker tournaments with friends. It helps to build a poker community in which gamblers can communicate with each other and join one game from any place in the world. Moreover, players can unite into leagues and those, who have excellent results, will be at the head of it. This best Android poker app is available for free on the Google Play Store and Apple App Store for users to download and enjoy. The more you stake, the more virtual chips you earn after winning. Moreover, you can promote within the game and reach a higher tier to unlock VIP elements.
    http://www.tea365.co.kr/bbs/board.php?bo_table=free&wr_id=6138
    Of course, you can always play for free if they offer free versions of their slots games. This can be a great way to test different slot machine types such as Vegas slots, or video slots with special features. Drake Casino offers some of the best casino slots available, both for free and real money games. The casino offers a wide range of slot choices, with new games added frequently to keep things exciting. The graphics are top-notch, and the gameplay is smooth and enjoyable. Bonus features and special promotions add extra excitement to the gaming experience. Overall, Drake Casino is a great online casino real money choice for players looking to enjoy quality casino slots at their leisure. Play-for-fun your favorite online casino games like slots, blackjack, roulette, and more on your desktop or mobile device at anytime and anywhere. Rivers Casino4Fun online site offers a wide selection of popular casino games (with new games added regularly), daily casino bonuses, and the chance to share the excitement and celebrate your big wins with other players!

    Reply

  50. I happen to be writing to make you be aware of of the outstanding experience my daughter found viewing yuor web blog. She mastered numerous pieces, which included what it’s like to have a very effective coaching spirit to get many people with ease learn certain specialized subject matter. You actually did more than people’s desires. Thank you for rendering the beneficial, healthy, edifying and as well as fun guidance on that topic to Emily.

    Reply

  53. I do not even know how I ended up here, but I thought this post was great. I do not know who you are but definitely you are going to a famous blogger if you aren’t already πŸ˜‰ Cheers!

    Reply

Leave a Comment

Ads Blocker Image Powered by Code Help Pro

Ads Blocker Detected!!!

We have detected that you are using extensions to block ads. Please support us by disabling these ads blockerπŸ™.

Powered By
Best Wordpress Adblock Detecting Plugin | CHP Adblock