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