Weka MCQs and Answers With Explanation: Weka is a widely used machine learning software that provides a comprehensive platform for data mining, predictive modeling, and statistical analysis. To master this technology, it is crucial to have a strong foundation of its concepts and functionalities. The Weka MCQs and answers provide a valuable resource for individuals seeking to improve their knowledge and skills in the field of machine learning.
Weka MCQs and Answers
In this Weka Quiz, we have compiled the top 55 Weka MCQs and answers that cover various aspects of the technology, including data preprocessing, classification, clustering, and association rules. These Weka Multiple Choice Questions and Answers come with detailed explanations that will assist users in comprehending the fundamentals of Weka.
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Weka Multiple Choice Questions and Answers
| Name | Weka |
| Exam Type | MCQ (Multiple Choice Questions) |
| Category | Technical Quiz |
| Mode of Quiz | Online |
Top 55 Weka MCQs | Weka Quiz
1. Which of the following is true about Weka?
a. Weka is a data visualization tool.
b. Weka is a programming language.
c. Weka is a collection of machine learning algorithms.
d. Weka is used only for unsupervised learning.
Answer: c. Weka is a collection of machine learning algorithms.
Explanation: Weka stands for Waikato Environment for Knowledge Analysis and is a collection of machine learning algorithms and data preprocessing tools.
2. Which file format is commonly used to import data into Weka?
a. PDF
b. CSV
c. MP4
d. PNG
Answer: b. CSV.
Explanation: Weka can import data in various file formats, but CSV (Comma Separated Values) is the most commonly used file format for importing data.
3. Which of the following is NOT a type of data preprocessing available in Weka?
a. Attribute selection
b. Data cleaning
c. Data normalization
d. Data visualization
Answer: d. Data visualization.
Explanation: Weka does not have built-in data visualization tools, but it does provide various data preprocessing techniques such as attribute selection, data cleaning, and data normalization.
4. Which algorithm is used for classification in Weka?
a. Naive Bayes
b. K-means
c. Random Forest
d. PCA
Answer: a. Naive Bayes.
Explanation: Weka provides a variety of classification algorithms, including Naive Bayes, Decision Trees, Support Vector Machines, and more.
5. Which of the following is NOT a clustering algorithm in Weka?
a. K-means
b. DBSCAN
c. EM
d. Linear Regression
Answer: d. Linear Regression.
Explanation: Linear Regression is not a clustering algorithm, but a supervised learning algorithm used for regression tasks.
6. Which of the following is NOT a type of evaluation metric in Weka?
a. Accuracy
b. Precision
c. Recall
d. Distance
Answer: d. Distance.
Explanation: Distance is not an evaluation metric in Weka, but a concept used in clustering algorithms to measure the similarity or dissimilarity between data points.
7. Which of the following is a feature selection technique in Weka?
a. Principal Component Analysis (PCA)
b. Recursive Feature Elimination (RFE)
c. K-means clustering
d. K-nearest neighbors (KNN)
Answer: b. Recursive Feature Elimination (RFE).
Explanation: RFE is a feature selection technique that recursively removes the least important features from the dataset until a desired number of features is reached. Weka provides various feature selection techniques, including RFE, Correlation-based Feature Selection (CFS), and more.
8. Which of the following is a disadvantage of the K-nearest neighbors (KNN) algorithm in Weka?
a. It is computationally expensive
b. It requires large amounts of training data
c. It is sensitive to irrelevant features
d. It cannot handle categorical data
Answer: a. It is computationally expensive.
Explanation: KNN algorithm is computationally expensive as it requires calculating the distance between the query point and all the training data points, which can be time-consuming for large datasets.
9. Which of the following is an ensemble learning algorithm in Weka?
a. Linear Regression
b. Naive Bayes
c. Random Forest
d. K-means
Answer: c. Random Forest.
Explanation: Random Forest is an ensemble learning algorithm that combines multiple decision trees to make more accurate predictions. Weka provides various ensemble learning algorithms, including Bagging, Boosting, and more.
10. Which of the following is NOT a type of neural network available in Weka?
a. Multilayer Perceptron (MLP)
b. Radial Basis Function (RBF)
c. Convolutional Neural Network (CNN)
d. Decision Tree
Answer: d. Decision Tree.
Explanation: Decision Tree is not a type of neural network, but a machine learning algorithm used for classification and regression tasks.
11. Which of the following is a supervised learning algorithm in Weka?
a. K-means
b. DBSCAN
c. Naive Bayes
d. EM
Answer: c. Naive Bayes.
Explanation: Naive Bayes is a supervised learning algorithm used for classification tasks, where the target variable is known.
12. Which of the following is NOT a data preprocessing technique in Weka?
a. Data normalization
b. Data imputation
c. Data visualization
d. Data discretization
Answer: c. Data visualization.
Explanation: Weka does not have built-in data visualization tools, but it does provide various data preprocessing techniques such as data normalization, data imputation, and data discretization.
13. Which of the following is a feature extraction technique in Weka?
a. Principal Component Analysis (PCA)
b. Recursive Feature Elimination (RFE)
c. Correlation-based Feature Selection (CFS)
d. K-nearest neighbors (KNN)
Answer: a. Principal Component Analysis (PCA).
Explanation: PCA is a feature extraction technique that transforms the original features into a smaller set of uncorrelated features that explain most of the variance in the data. Weka provides various feature extraction techniques, including PCA, Linear Discriminant Analysis (LDA), and more.
14. Which of the following is NOT a type of attribute in Weka?
a. Numeric
b. Nominal
c. Binary
d. Sequential
Answer: d. Sequential.
Explanation: Sequential is not a type of attribute in Weka, but a concept used in time series analysis to represent data that is ordered in time.
15. Which of the following is NOT a data mining task in Weka?
a. Classification
b. Clustering
c. Association Rule Mining
d. Data Visualization
Answer: d. Data Visualization.
Explanation: Data Visualization is not a data mining task in Weka, but a technique used to represent data in a visual form for better understanding and insights.
16. Which of the following is a rule-based learning algorithm in Weka?
a. Random Forest
b. J48
c. K-means
d. DBSCAN
Answer: b. J48.
Explanation: J48 is a decision tree algorithm based on the C4.5 algorithm, which builds a tree of if-then rules to make predictions. Weka provides various rule-based learning algorithms, including ZeroR, OneR, and more.
17. Which of the following is a data imbalance problem in Weka?
a. Overfitting
b. Underfitting
c. Missing values
d. Class imbalance
Answer: d. Class imbalance.
Explanation: Class imbalance is a data imbalance problem that occurs when one class in the dataset has significantly fewer samples than the other classes, leading to biased predictions. Weka provides various techniques to handle class imbalance, including resampling, cost-sensitive learning, and more.
18. Which of the following is NOT a type of regression algorithm in Weka?
a. Linear Regression
b. Polynomial Regression
c. Logistic Regression
d. K-means
Answer: d. K-means.
Explanation: K-means is not a regression algorithm, but a clustering algorithm used to group similar data points together.
19. Which of the following is NOT a type of cross-validation in Weka?
a. K-fold cross-validation
b. Leave-one-out cross validation
c. Stratified cross-validation
d. Naive Bayes cross-validation
Answer: d. Naive Bayes cross-validation.
Explanation: Naive Bayes cross-validation is not a type of cross-validation in Weka, but a technique used to evaluate the performance of Naive Bayes classifier on a dataset.
20. Which of the following is a data discretization technique in Weka?
a. Equal width discretization
b. Normalization
c. Principal Component Analysis (PCA)
d. Recursive Feature Elimination (RFE)
Answer: a. Equal width discretization.
Explanation: Equal width discretization is a data discretization technique that divides the range of values into equal-width intervals and assigns a discrete value to each interval. Weka provides various data discretization techniques, including equal frequency discretization, unsupervised discretization, and more.
21. Which of the following is NOT a type of ensemble learning algorithm in Weka?
a. Bagging
b. Boosting
c. Random Forest
d. K-means
Answer: d. K-means.
Explanation: K-means is not an ensemble learning algorithm, but a clustering algorithm used to group similar data points together. Weka provides various ensemble learning algorithms, including Bagging, Boosting, and Random Forest.
22. Which of the following is a dimensionality reduction technique in Weka?
a. Principal Component Analysis (PCA)
b. Recursive Feature Elimination (RFE)
c. Correlation-based Feature Selection (CFS)
d. K-nearest neighbors (KNN)
Answer: a. Principal Component Analysis (PCA).
Explanation: PCA is a dimensionality reduction technique that transforms the original features into a smaller set of uncorrelated features that explain most of the variance in the data. Weka provides various dimensionality reduction techniques, including PCA, Linear Discriminant Analysis (LDA), and more.
23. Which of the following is a non-parametric classification algorithm in Weka?
a. Logistic Regression
b. Decision Tree
c. Naive Bayes
d. k-Nearest Neighbors (k-NN)
Answer: d. k-Nearest Neighbors (k-NN).
Explanation: k-NN is a non-parametric classification algorithm that uses the k-nearest neighbors to classify a new instance based on the majority class of its neighbors. Weka provides various non-parametric classification algorithms, including k-NN, Random Forest, and more.
24. Which of the following is a neural network activation function available in Weka?
a. Sigmoid
b. ReLU
c. Tanh
d. All of the above
Answer: d. All of the above.
Explanation: Weka provides various neural network activation functions, including Sigmoid, ReLU, Tanh, and more.
25. Which of the following is a clustering evaluation metric in Weka?
a. Accuracy
b. F-measure
c. Silhouette coefficient
d. Precision
Answer: c. Silhouette coefficient.
Explanation: Silhouette coefficient is a clustering evaluation metric that measures the quality of clustering by comparing the distance between the data points within the same cluster and the distance between the data points of different clusters. Weka provides various clustering evaluation metrics, including Silhouette coefficient, Sum of Squared Error (SSE), and more.
26. Which of the following is a data imbalance handling technique in Weka?
a. Bagging
b. SMOTE
c. Random Forest
d. Boosting
Answer: b. SMOTE.
Explanation: SMOTE (Synthetic Minority Over-sampling Technique) is a data imbalance handling technique that creates synthetic samples of the minority class by interpolating between the existing minority class samples. Weka provides various data imbalance handling techniques, including SMOTE, ADASYN, and more.
27. Which of the following is a regression algorithm in Weka?
a. Decision Tree
b. k-Nearest Neighbors (k-NN)
c. Linear Regression
d. Support Vector Machine (SVM)
Answer: c. Linear Regression.
Explanation: Linear Regression is a regression algorithm that models the relationship between the dependent variable and one or more independent variables by fitting a linear equation to the data. Weka provides various regression algorithms, including Linear Regression, Multilayer Perceptron (MLP), and more.
28. Which of the following is a data normalization technique in Weka?
a. Min-max normalization
b. Recursive Feature Elimination (RFE)
c. Correlation-based Feature Selection (CFS)
d. Principal Component Analysis (PCA)
Answer: a. Min-max normalization.
Explanation: Min-max normalization is a data normalization technique that scales the data to a fixed range of values between 0 and 1. Weka provides various data normalization techniques, including z-score normalization, decimal scaling, and more.
29. Which of the following is NOT a type of attribute selection in Weka?
a. Wrapper Subset Evaluator
b. Filter Subset Evaluator
c. Correlation-based Feature Selection (CFS)
d. Boosting
Answer: d. Boosting.
Explanation: Boosting is not a type of attribute selection, but an ensemble learning algorithm used for classification and regression tasks. Weka provides various attribute selection techniques, including Wrapper Subset Evaluator, Filter Subset Evaluator, and more.
30. Which of the following is a classification algorithm in Weka?
a. Support Vector Machine (SVM)
b. k-Means
c. Hierarchical Clustering
d. PCA
Answer: a. Support Vector Machine (SVM).
Explanation: SVM is a classification algorithm that separates the data into different classes by finding the hyperplane that maximally separates the classes. Weka provides various classification algorithms, including SVM, Naive Bayes, and more.
31. Which of the following is NOT a type of ensemble learning in Weka?
a. AdaBoost
b. Bagging
c. Boosting
d. Random Forest
Answer: d. Random Forest.
Explanation: Random Forest is not a type of ensemble learning, but a specific ensemble learning algorithm that uses decision trees as the base classifiers. Weka provides various ensemble learning algorithms, including AdaBoost, Bagging, and Boosting.
32. Which of the following is a distance metric used in k-Nearest Neighbors (k-NN) algorithm in Weka?
a. Euclidean distance
b. Manhattan distance
c. Mahalanobis distance
d. All of the above
Answer: d. All of the above.
Explanation: Weka provides various distance metrics used in k-Nearest Neighbors (k-NN) algorithm, including Euclidean distance, Manhattan distance, and Mahalanobis distance.
33. Which of the following is a missing value handling technique in Weka?
a. Mean imputation
b. Median imputation
c. Mode imputation
d. All of the above
Answer: d. All of the above.
Explanation: Weka provides various missing value handling techniques, including mean imputation, median imputation, mode imputation, and more.
34. Which of the following is a kernel function used in Support Vector Machine (SVM) algorithm in Weka?
a. Linear kernel
b. Polynomial kernel
c. Gaussian kernel
d. All of the above
Answer: d. All of the above.
Explanation: Weka provides various kernel functions used in Support Vector Machine (SVM) algorithm, including linear kernel, polynomial kernel, and Gaussian kernel.
35. Which of the following is a rule-based classifier in Weka?
a. Decision Tree
b. Naive Bayes
c. ZeroR
d. JRip
Answer: d. JRip.
Explanation: JRip is a rule-based classifier in Weka that constructs a set of rules from the data that classify the instances based on their attribute values. Weka provides various rule-based classifiers, including JRip, PART, and more.
36. Which of the following is NOT a type of clustering algorithm in Weka?
a. k-Means
b. Hierarchical Clustering
c. DBSCAN
d. Linear Regression
Answer: d. Linear Regression.
Explanation: Linear Regression is not a type of clustering algorithm, but a regression algorithm used to model the relationship between the dependent variable and one or more independent variables. Weka provides various clustering algorithms, including k-Means, Hierarchical Clustering, DBSCAN, and more.
37. Which of the following is a feature selection technique that selects a subset of features based on their correlation with the class attribute in Weka?
a. Wrapper Subset Evaluator
b. Filter Subset Evaluator
c. Correlation-based Feature Selection (CFS)
d. Principal Component Analysis (PCA)
Answer: c. Correlation-based Feature Selection (CFS).
Explanation: CFS is a feature selection technique in Weka that selects a subset of features based on their correlation with the class attribute. Weka provides various feature selection techniques, including Wrapper Subset Evaluator, Filter Subset Evaluator, and more.
38. Which of the following is a rule induction algorithm in Weka?
a. k-NN
b. Apriori
c. Random Forest
d. JRip
Answer: d. JRip.
Explanation: JRip is a rule induction algorithm in Weka that constructs a set of rules from the data that classify the instances based on their attribute values. Weka provides various rule induction algorithms, including JRip, PART, and more.
39. Which of the following is a type of ensemble learning technique in Weka?
a. Decision Tree
b. Naive Bayes
c. Bagging
d. k-NN
Answer: c. Bagging.
Explanation: Bagging is a type of ensemble learning technique in Weka that constructs multiple models from different subsets of the data and combines them to improve the predictive performance. Weka provides various ensemble learning techniques, including Bagging, Boosting, and more.
40. Which of the following is a dimensionality reduction technique that maximizes the margin between classes in Weka?
a. PCA
b. LDA
c. ICA
d. SVM
Answer: b. LDA.
Explanation: LDA is a dimensionality reduction technique in Weka that maximizes the margin between classes by finding the linear combinations of features that best separate the classes. SVM is a classification algorithm that can use LDA as a preprocessing step. Weka provides various dimensionality reduction techniques, including PCA, LDA, and more.
41. Which of the following is a type of classification algorithm in Weka that assigns a class label based on the most common class in the training data?
a. Decision Tree
b. Naive Bayes
c. k-NN
d. ZeroR
Answer: d. ZeroR.
Explanation: ZeroR is a type of classification algorithm in Weka that assigns a class label based on the most common class in the training data. It is a simple baseline classifier used to evaluate the predictive performance of more complex classifiers. Weka provides various classification algorithms, including Decision Tree, Naive Bayes, k-NN, and more.
42. Which of the following is a type of ensemble learning technique that combines multiple models using weighted voting in Weka?
a. Bagging
b. Boosting
c. Stacking
d. Random Forest
Answer: c. Stacking.
Explanation: Stacking is a type of ensemble learning technique in Weka that combines multiple models using weighted voting. The output of the base models is used as input to a meta-model that learns how to combine them to make the final prediction. Weka provides various ensemble learning techniques, including Bagging, Boosting, Random Forest, and more.
43. Which of the following is a feature selection technique that evaluates the subsets of features using a learning algorithm in Weka?
a. Wrapper Subset Evaluator
b. Filter Subset Evaluator
c. Correlation-based Feature Selection (CFS)
d. Principal Component Analysis (PCA)
Answer: a. Wrapper Subset Evaluator.
Explanation: Wrapper Subset Evaluator is a feature selection technique in Weka that evaluates the subsets of features using a learning algorithm. It searches through the space of possible feature subsets and selects the one that achieves the best performance on the validation set. Weka provides various feature selection techniques, including Wrapper Subset Evaluator, Filter Subset Evaluator, and more
44. Which of the following is a clustering algorithm in Weka that uses a density-based approach?
a. k-Means
b. EM
c. DBSCAN
d. SOM
Answer: c. DBSCAN.
Explanation: DBSCAN is a clustering algorithm in Weka that uses a density-based approach to group the instances into clusters. It works by identifying the dense regions of the data and connecting them into clusters. Weka provides various clustering algorithms, including k-Means, EM, DBSCAN, SOM, and more.
45. Which of the following is a method for handling missing values in Weka that uses the available data to estimate the missing values?
a. Mean Imputation
b. Mode Imputation
c. Median Imputation
d. k-NN Imputation
Answer: d. k-NN Imputation.
Explanation: k-NN Imputation is a method for handling missing values in Weka that uses the available data to estimate the missing values. It works by finding the k nearest instances to the instance with missing values and using their attribute values to estimate the missing values. Weka provides various methods for handling missing values, including Mean Imputation, Mode Imputation, Median Imputation, k-NN Imputation, and more.
46. Which of the following is a type of ensemble learning technique in Weka that combines multiple models using a weighted sum of their predictions?
a. Bagging
b. Boosting
c. Stacking
d. Random Forest
Answer: b. Boosting.
Explanation: Boosting is a type of ensemble learning technique in Weka that combines multiple models using a weighted sum of their predictions. It works by iteratively reweighting the instances based on their classification errors and building a new model on the reweighted data. Weka provides various ensemble learning techniques, including Bagging, Boosting, Random Forest, Stacking, and more.
47. Which of the following is a type of classification algorithm in Weka that models the joint probability distribution of the features and the class?
a. Naive Bayes
b. k-NN
c. Decision Tree
d. SVM
Answer: a. Naive Bayes.
Explanation: Naive Bayes is a type of classification algorithm in Weka that models the joint probability distribution of the features and the class using Bayes’ theorem and the assumption of independence between the features. Weka provides various classification algorithms, including Naive Bayes, k-NN, Decision Tree, SVM, and more.
48. Which of the following is a clustering algorithm in Weka that uses a probabilistic approach?
a. k-Means
b. EM
c. DBSCAN
d. SOM
Answer: b. EM.
Explanation: EM is a clustering algorithm in Weka that uses a probabilistic approach to group the instances into clusters. It works by modeling the data as a mixture of probability distributions and estimating the parameters of the distributions using the Expectation-Maximization algorithm. Weka provides various clustering algorithms, including k-Means, EM, DBSCAN, SOM, and more.
49. Which of the following is a feature selection technique that evaluates the subsets of features based on their predictive power and selects the subset that gives the best performance?
a. Filter
b. Wrapper
c. Embedded
d. Correlation-based
Answer: b. Wrapper.
Explanation: Wrapper is a feature selection technique in Weka that evaluates the subsets of features based on their predictive power and selects the subset that gives the best performance. It works by using a learning algorithm to train and evaluate the model on each subset of features and selecting the subset that gives the best performance. Weka provides various feature selection techniques, including Filter, Wrapper, Embedded, Correlation-based, and more.
50. Which of the following is a type of dimensionality reduction technique in Weka that maps the high-dimensional data to a lower-dimensional space while preserving the pairwise distances between the instances?
a. Principal Component Analysis (PCA)
b. Linear Discriminant Analysis (LDA)
c. t-SNE
d. Isomap
Answer: d. Isomap.
Explanation: Isomap is a type of dimensionality reduction technique in Weka that maps the high-dimensional data to a lower-dimensional space while preserving the pairwise distances between the instances. It works by constructing a neighborhood graph of the instances and estimating the geodesic distances between them using a shortest path algorithm. Weka provides various dimensionality reduction techniques, including PCA, LDA, t-SNE, Isomap, and more.
51. Which of the following is a type of rule-based classification algorithm in Weka that builds a set of rules from the data?
a. OneR
b. ZeroR
c. JRip
d. Random Tree
Answer: c. JRip.
Explanation: JRip is a type of rule-based classification algorithm in Weka that builds a set of rules from the data. It works by iteratively adding rules to the rule set based on the accuracy and coverage of the rules. Weka provides various classification algorithms, including OneR, ZeroR, JRip, Random Tree, and more.
52. Which of the following is a type of clustering algorithm in Weka that uses a hierarchical approach to group the instances into clusters?
a. k-Means
b. EM
c. DBSCAN
d. Hierarchical
Answer: d. Hierarchical.
Explanation: Hierarchical is a type of clustering algorithm in Weka that uses a hierarchical approach to group the instances into clusters. It works by recursively merging the most similar clusters based on a distance metric until all the instances are in a single cluster. Weka provides various clustering algorithms, including k-Means, EM, DBSCAN, Hierarchical, and more.
53. Which of the following is a type of feature selection technique in Weka that selects the features based on their correlation with the class and removes the redundant features?
a. Filter
b. Wrapper
c. Embedded
d. Correlation-based
Answer: d. Correlation-based.
Explanation: Correlation-based is a feature selection technique in Weka that selects the features based on their correlation with the class and removes the redundant features. It works by computing the correlation between each feature and the class and selecting the subset of features with the highest correlation. Weka provides various feature selection techniques, including Filter, Wrapper, Embedded, Correlation-based, and more.
54. Which of the following is a type of clustering algorithm in Weka that uses a grid-based approach to group the instances into clusters?
a. k-Means
b. EM
c. DBSCAN
d. CLIQUE
Answer: d. CLIQUE.
Explanation: CLIQUE is a type of clustering algorithm in Weka that uses a grid-based approach to group the instances into clusters. It works by partitioning the data into overlapping grids and identifying the dense regions of the data within each grid. Weka provides various clustering algorithms, including k-Means, EM, DBSCAN, CLIQUE, and more.
55. Which of the following is a type of classification algorithm in Weka that builds a decision tree from the data?
a. Naive Bayes
b. k-NN
c. J48
d. Random Forest
Answer: c. J48.
Explanation: J48 is a type of classification algorithm in Weka that builds a decision tree from the data. It works by recursively splitting the data based on the attribute that provides the most information gain until a stopping criterion is met. Weka provides various classification algorithms, including Naive Bayes, k-NN, J48, Random Forest, and more.
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