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=θ^T.X, θ is the feature set and X is the “weight vector” or “confidences 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 𝜃^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, “cold start” happens when you have a large dataset of users who have rated only a limited number of items.
- True
- False
