WorksheetsML SLIV
Total questions: 60
Worksheet time: 30mins
What is K-means primarily used for?
Dimensionality reduction
Clustering
Classification
Regression
Which algorithm is used for density-based clustering?
K-means
B) DBSCAN
C) t-SNE
D) PCA
Which of the following is an unsupervised learning algorithm?
A) Decision Trees
B) DBSCAN
C) t-SNE
D) Factorization of non-negative matrices
What does t-SNE stand for?
A) T-distributed Stochastic Neighbor Embedding
B) Tree-Structured Neighborhood Embedding
C) Time Series Network Evaluation
D) Title Statistics Nether Embed
In K-means clustering, how is the number of clusters determined?
A) By the user input
B) Automatically by the algorithm
C) Randomly
D) Typically
Which algorithm is sensitive to outliers?
A) K-means
B) DBSCAN
C) Decision Trees
D) Random Forest
Which of the following is a hierarchical clustering algorithm?
A)K-means
B)DBSCAN
C)t-SNE
D)Factorization of non-negative matrices
What is the main disadvantage of Decision Trees?
A)They are prone to overfitting
B)They cannot handle categorical data
C)They are computationally expensive
D)All the above
Which clustering algorithm does not require the user to specify the number of clusters?
A)K-means
B)DBSCAN
C)Decision Trees
D)PCA
What type of machine learning algorithm is t-SNE?
A)Supervised
B)Unsupervised
C) Reinforcement
D)Supervisor
What is the output of DBSCAN clustering?
A)Labels
B)Centroids
C)Noise points
D)Variables
Which algorithm can be used for both regression and classification?
A)K-means
B)Decision Trees
C)DBSCAN
D)PCA
In t-SNE, what does the perplexity parameter control?
A)The number of nearest neighbors to consider
B)The learning rate
C)The number of iterations
D)All the above
What is the purpose of using Decision Trees in ensemble methods like Random Forest?
A)To reduce bias
B)To reduce variance
C)To speed up training
D)All the above
Which algorithm is not sensitive to the order of input data points?
A)K-means
B)t-SNE
D)Factorization of non-negative matrices
C)DBSCAN
Which algorithm can handle non-linear data?
A)K-means
B)DBSCAN
C)Decision Trees
D)Linear regression
What does the 'k' in K-means represent?
A)Number of iterations
B)Number of clusters
C)Number of dimensions
D)Number of regression
Which algorithm is suitable for visualizing high-dimensional data in lower dimensions?
A)Decision Trees
B)t-SNE
C)DBSCAN
D)Linear regression
Which algorithm is sensitive to the choice of distance metric?
A)K-means
B)DBSCAN
C)t-SNE
D)PCA
How does Decision Trees handle missing values?
A)By imputing the most common value
B)By skipping the missing value during split evaluation
C)By imputing the mean value
D)By imputing categorical variables
Which algorithm can produce a visual representation of the data in 2D or 3D?
A)DBSCAN
B)K-means
C)t-SNE
D)Factorization of non-negative matrices
Which algorithm is known for its interpretability?
A)K-means
B)DBSCAN
C)Decision Trees
D)Random Forest
Which clustering algorithm can detect arbitrarily shaped clusters?
A)K-means
B)DBSCAN
C)Decision Trees
D)PCA
Which algorithm is used for reducing the dimensionality of data while preserving the local structure?
A)PCA
B)t-SNE
C)K-means
D)Linear regression
In Decision Trees, what is entropy used for?
A)To measure impurity
B)To calculate the distance between points
C)To determine the number of clusters
D)To determine the variables
Which of the following is NOT a supervised learning algorithm?
A)Decision Trees
B)K-Means Clustering
C)Support Vector Machines
D)Linear Regression
Which algorithm is commonly used for feature selection and dimensionality reduction?
A)PCA
B)AdaBoost
C)Gradient Boosting
D)Decision Trees
Which algorithm is used for both classification and regression tasks and is based on the concept of decision trees?
A)K-Means
B)SVM
C)Random Forest
D)DBSCAN
What type of machine learning algorithm is commonly used for grouping similar data points together into clusters based on their features, without needing labeled data?
A)Supervised Learning
B)Unsupervised Learning
C)Reinforcement Learning
D)Semi-supervised Learning
Non-Negative Matrix Factorization (NMF) is a technique used in machine learning for:
A)Dimensionality reduction and feature extraction
B)Clustering and classification
C)Regression analysis
D)Association rule mining
Which algorithm is sensitive to the initialization of cluster centroids?
A)K-means
B)DBSCAN
C)Decision Trees
D)Random Forest
Which algorithm is used for feature selection?
A)K-means
B)DBSCAN
C)Decision Trees
D)PCA
Which algorithm is used for imputing missing values?
A)K-means
B)DBSCAN
C)Decision Trees
D)Factorization of non-negative matrices
Which algorithm is prone to the curse of dimensionality?
A)K-means
B)DBSCAN
C)t-SNE
D)Linear regression
Which algorithm uses a dendrogram to represent the hierarchy of clusters?
A)K-means
B)DBSCAN
C)Hierarchical clustering
D)Factorization of non-negative matrices
Which algorithm is used for outlier detection?
A)K-means
B)DBSCAN
C)Decision Trees
D)Categorical variables
Which algorithm is computationally expensive for large datasets?
A)K-means
B)DBSCAN
C)t-SNE
D)PCA
Which algorithm is not guaranteed to converge to the global optimum?
A)K-means
B)DBSCAN
C)Decision Trees
D)PCA
Which algorithm is suitable for handling datasets with noise?
A)K-means
B)DBSCAN
C)t-SNE
D)PCA
Which algorithm uses the concept of centroids?
A)Decision Trees
B)DBSCAN
C)K-means
D)Factorization of non-negative matrices
Which algorithm is used for manifold learning?
A)Decision Trees
B)t-SNE
C)K-means
D)PCA
Which algorithm is used for both regression and classification tasks?
A)Decision Trees
B)t-SNE
C)K-means
D)Random Forest
Which algorithm can handle categorical data without preprocessing?
A)K-means
B)DBSCAN
C)Decision Trees
D)PCA
Which algorithm is used for reducing the number of features?
A)DBSCAN
B)K-means
C)PCA
D)t-SNE
Which algorithm has a parameter called 'min_samples'?
A)K-means
B)DBSCAN
C)t-SNE
D)PCA
Which algorithm can handle non-convex clusters?
A)K-means
B)DBSCAN
C)Decision Trees
D)Logistic regression
Which algorithm is based on the concept of proximity?
A)K-means
B)DBSCAN
С) PCA
D) t-SNE
Which algorithm is used for ensemble learning?
A)K-means
B)DBSCAN
C)Decision Trees
D)PCA
What is the main drawback of K-means clustering?
A)It doesn't work well with high-dimensional data
B)It requires a predetermined number of clusters
C)It is sensitive to outliers
D)All the above
Which algorithm is based on the concept of nearest neighbors?
A)K-means
B)DBSCAN
C)t-SNE
D)All the above
Which algorithm requires scaling of features before application?
A)K-means
B)DBSCAN
C)Decision Trees
D)t-SNE
What is the main advantage of using Decision Trees?
A)They are insensitive to outliers
B)They can handle both numerical and categorical data
C)They always converge to the global optimum
D)They always converge to the optimal variables
Which algorithm is used for anomaly detection?
A)K-means
B)DBSCAN
C)Decision Trees
D)PCA
Which algorithm is sensitive to the choice of initial cluster centroids?
A)K-means
B)DBSCAN
C)t-SNE
D)PCA
Which algorithm is commonly used for data preprocessing?
A)K-means
B)DBSCAN
C)t-SNE
D)PCA
What is the main objective of Non-Negative Matrix Factorization (NMF)?
A) Minimize the reconstruction error between the original matrix and its factorized form
B) Maximize the variance of the data points
C) Minimize the regularization term to prevent overfitting
D) Maximize the likelihood of the observed data given the model parameters
Which of the following techniques is commonly used for encoding categorical variables into numerical format for machine learning models?
A)One-Hot Encoding
B)Mean Encoding
C)Label Encoding
D)Ordinal Encoding
In linear regression, what is the objective of the model?
A)To minimize the sum of squared errors between predicted and actual values
B)To maximize the likelihood of the observed data given the model parameters
C)To find the optimal split points in the feature space
D)To minimize the regularization term to prevent overfitting
What is the main objective of the K-Means clustering algorithm?
A)To maximize the likelihood of the observed data given the model parameters
B)To minimize the reconstruction error between the original data and its low-dimensional representation
C)To reduce the dimensionality of high-dimensional data while preserving local and global structure
D)To partition a dataset into K clusters where each data point belongs to the cluster with the nearest mean
What is the significance of the epsilon (ε) parameter in the DBSCAN algorithm?
A)It defines the minimum number of points required to form a cluster
B)It specifies the maximum distance between points to be considered as neighbors
C)It determines the number of clusters to be formed
D)It controls the trade-off between precision and recall in clustering
