ML

Q35. Which of the following sequences is correct for a K-Means algorithm using Forgy method of initialization?

(1,2,3,5,4) 1. Specify the number of clusters, 2. Assign cluster centroids randomly, 3. Assign each data point to the nearest cluster centroid 5. Re-compute cluster centroids, 4. Re-assign each point to nearest cluster centroids

Q5. What is the minimum no. of variables/ features required to perform clustering?

1

Q31. What is true about K-Mean Clustering?

1. K-means is extremely sensitive to cluster center initializations, 2. Bad initialization can lead to Poor convergence speed, 3. Bad initialization can lead to bad overall clustering

Q17. Which of the following metrics, do we have for finding dissimilarity between two clusters in hierarchical clustering?

1. Single-link, 2. Complete-link, 3. Average-link

Q27. Assume, you want to cluster 7 observations into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has following observations: C1: {(2,2), (4,4), (6,6)} C2: {(0,4), (4,0)} C3: {(5,5), (9,9)} What will be the Manhattan distance for observation (9, 9) from cluster centroid C1. In second iteration.

10 -> Manhattan distance between centroid C1 i.e. (4, 4) and (9, 9) = (9-4) + (9-4) = 10

Q15. What is the most appropriate no. of clusters for the data points represented by the following dendrogram:

4 -> The best choice of the no. of clusters is the no. of vertical lines in the dendrogram cut by a horizontal line that can transverse the maximum distance vertically without intersecting a cluster.

22) PCA works better if there is?

A linear structure in the data, If variables are scaled in the same unit

Q39. Which of the following are the high and low bounds for the existence of F-Score?

A. [0,1]

Q12. How can Clustering (Unsupervised Learning) be used to improve the accuracy of Linear Regression model (Supervised Learning):

All of the above: 1. Creating different models for different cluster groups. 2. Creating an input feature for cluster ids as an ordinal variable. 3. Creating an input feature for cluster centroids as a continuous variable. 4. Creating an input feature for cluster size as a continuous variable.

Q8. Which of the following can act as possible termination conditions in K-Means?

All of the above: 1. For a fixed number of iterations. 2. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum. 3. Centroids do not change between successive iterations. 4. Terminate when RSS falls below a threshold.

8) The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA?

All of the above: 1. PCA is an unsupervised method, 2. It searches for the directions that data have the largest variance, 3. Maximum number of principal components <= number of features, 4. All principal components are orthogonal to each other

Q13. What could be the possible reason(s) for producing two different dendrograms using agglomerative clustering algorithm for the same dataset?

All of the above: 1. Proximity function used, 2. of data points used, 3. of variables used

Q32. Which of the following can be applied to get good results for K-means algorithm corresponding to global minima?

All of the above: 1. Try to run algorithm for different centroid initialization, 2. Adjust number of iterations, 3. Find out the optimal number of clusters

15) In t-SNE algorithm, which of the following hyper parameters can be tuned?

All of the above: a. Number of dimensions, b. Smooth measure of effective number of neighbors, c. Maximum number of iterations

Q36. If you are using Multinomial mixture models with the expectation-maximization algorithm for clustering a set of data points into two clusters, which of the assumptions are important:

All the data points follow two multinomial distribution

20) Which of the following comparison(s) are true about PCA and LDA?

Both LDA and PCA are linear transformation techniques, LDA is supervised whereas PCA is unsupervised, 3PCA maximize the variance of the data, whereas LDA maximize the separation between different classes

Q26. Assume, you want to cluster 7 observations into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has following observations: C1: {(2,2), (4,4), (6,6)} C2: {(0,4), (4,0)} C3: {(5,5), (9,9)} What will be the cluster centroids if you want to proceed for second iteration?

C1: (4,4), C2: (2,2), C3: (7,7) -> Finding centroid for data points in cluster C1 = ((2+4+6)/3, (2+4+6)/3) = (4, 4)

Q4. Which of the following is the most appropriate strategy for data cleaning before performing clustering analysis, given less than desirable number of data points:

Capping and flouring of variables

Q18. Which of the following are true?

Clustering analysis is negatively affected by multicollinearity of features

Q1. Movie Recommendation systems are an example of:

Clustering and Reinforcement Learning

Q16. In which of the following cases will K-Means clustering fail to give good results?

Data points with outliers, Data points with different densities, and Data points with non-convex shapes

10) In which of the following scenarios is t-SNE better to use than PCA for dimensionality reduction while working on a local machine with minimal computational power?

Dataset with 10,000 entries and 8 features

Q30. Which of the following method is used for finding optimal of cluster in K-Mean algorithm?

Elbow method

Q37. Which of the following is/are not true about Centroid based K-Means clustering algorithm and Distribution based expectation-maximization clustering algorithm:

Expectation maximization algorithm is a special case of K-Means

Q25. K-Mean algorithm has some limitations. One of the limitation it has is, it makes hard assignments(A point either completely belongs to a cluster or not belongs at all) of points to clusters. Note: Soft assignment can be consider as the probability of being assigned to each cluster: say K = 3 and for some point xn, p1 = 0.7, p2 = 0.2, p3 = 0.1) 1 Which of the following algorithm(s) allows soft assignments?

Gaussian mixture models Fuzzy K-means

9) Suppose we are using dimensionality reduction as pre-processing technique, i.e., instead of using all the features, we reduce the data to k dimensions with PCA. And then use these PCA projections as our features. Which of the following statement is correct?

Higher 'k' means less regularization

Q28. If two variables V1 and V2, are used for clustering. Which of the following are true for K means clustering with k =3?

If V1 and V2 has a correlation of 1, the cluster centroids will be in a straight line

19) In which of the following case LDA will fail?

If the discriminatory information is not in the mean but in the variance of the data

Q24. Which of the following is/are valid iterative strategy for treating missing values before clustering analysis?

Imputation with Expectation Maximization algorithm

Q29. Feature scaling is an important step before applying K-Mean algorithm. What is reason behind this?

In distance calculation it will give the same weights for all features

27) Which of the following options are correct, when you are applying PCA on a image dataset?

It can be used for lossy image compression. It is not invariant to shadows.

Q38. Which of the following is/are not true about DBSCAN clustering algorithm:

It has strong assumptions for the distribution of data points in dataspace, It has substantially high time complexity of order O(n3)

11) Which of the following statement is true for a t-SNE cost function?

It is symmetric in nature.

Q9. Which of the following clustering algorithms suffers from the problem of convergence at local optima?

K- Means clustering algorithm, Expectation-Maximization clustering algorithm

Q10. Which of the following algorithm is most sensitive to outliers?

K-means clustering algorithm

Q6. For two runs of K-Mean clustering is it expected to get same clustering results?

No, it's possible to receive same clustering results from K-means by setting the same seed value for each run. But that is done by simply making the algorithm choose the set of same random no. for each run.

6) Which of the following algorithms cannot be used for reducing the dimensionality of data?

None of these (t-SNE, PCA, LDA False)

21) What will happen when eigenvalues are roughly equal?

PCA will perform badly

Q2. Sentiment Analysis is an example of:

Regression, Classification, and Reinforcement Learning

4) Which of the following techniques would perform better for reducing dimensions of a data set?

Removing columns which have too many missing values

23) What happens when you get features in lower dimensions using PCA?

The features will lose interpretability, The features may not carry all information present in data

5) [ True or False ] Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.

True because Reducing the dimension of data will take less time to train a model.

3) I have 4 variables in the dataset such as - A, B, C & D. I have performed the following actions: Step 1: Using the above variables, I have created two more variables, namely E = A + 3 * B and F = B + 5 * C + D., Step 2: Then using only the variables E and F I have built a Random Forest model. Could the steps performed above represent a dimensionality reduction method?

True because Step 1 could be used to represent the data into 2 lower dimensions.

7) [ True or False ] PCA can be used for projecting and visualizing data in lower dimensions.

True, We can take the first 2 principal components and then visualize the data using scatter plot.

2) [ True or False ] It is not necessary to have a target variable for applying dimensionality reduction algorithms.

True, it is not necessary to have a target variable

13) [True or False] t-SNE learns non-parametric mapping.

True, t-SNE learns a non-parametric mapping, which means that it does not learn an explicit function that maps data from the input space to the map.

16) What is of the following statement is true about t-SNE in comparison to PCA?

When the data is huge (in size), t-SNE may fail to produce better results.

Q3. Can decision trees be used for performing clustering?

Yes, Decision Trees can be used for performing clustering

1) Imagine, you have 1000 input features and 1 target feature in a machine learning problem. You have to select 100 most important features based on the relationship between input features and the target features. Do you think, this is an example of dimensionality reduction?

Yes, it is an example of dimensionality reduction

Q7. Is it possible that Assignment of observations to clusters does not change between successive iterations in K-Means

Yes, when the K-Means algorithm has reached the local or global minima, it will not alter the assignment of data points to clusters for two successive iterations.

25) Which of the following option(s) is / are true?

You don't need to initialize parameters in PCA, PCA can't be trapped into local minima problem. Explanation: PCA is a deterministic algorithm which doesn't have parameters to initialize and it doesn't have local minima problem like most of the machine learning algorithms has.

14) Which of the following statement is correct for t-SNE and PCA?

t-SNE is nonlinear whereas PCA is linear

12) Imagine you are dealing with text data. To represent the words you are using word embedding (Word2vec). In word embedding, you will end up with 1000 dimensions. Now, you want to reduce the dimensionality of this high dimensional data such that, similar words should have a similar meaning in nearest neighbor space.In such case, which of the following algorithm are you most likely choose?

t-SNE which stands for t-Distributed Stochastic Neighbor Embedding which consider the nearest neighbors for reducing the data.