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Latest Python clustering techniques MCQ for Students
Which Python library is frequently used for visualizing data when working with DBSCAN?
a. Numpy
b. Pandas
c. Matplotlib
d. Seaborn
Option c – Matplotlib
What is the computational complexity of the DBSCAN clustering algorithm?
a. O(n)
b. O(n²)
c. O(n log n)
d. O(n² log n)
Option b – O(n²)
Which clustering method’s results are affected by the sequence in which data points are processed?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Gaussian Mixture Models
Option a – K-means clustering
Which Python package is typically used to compute pairwise distances between points for DBSCAN?
a. Numpy
b. Pandas
c. Scikit-learn
d. Scipy
Option d – Scipy
What is the effect of setting a very high MinPts value in DBSCAN?
a. More points are marked as noise
b. More clusters are detected
c. Fewer clusters are identified
d. The algorithm fails to converge
Option c – Fewer clusters are identified
Which data structure is most often used to track whether data points have been visited or not during DBSCAN?
a. List
b. Set
c. Queue
d. Stack
Option b – Set
In DBSCAN, which process is responsible for grouping points into clusters?
a. Density-reachability assessment
b. Density-connected components exploration
c. Both (a) and (b)
d. Neither (a) nor (b)
Option c – Both (a) and (b)
Which metrics can be used to evaluate how well clustering has performed?
a. Silhouette score
b. Rand index
c. Davies-Bouldin index
d. All of the above
Option d – All of the above
For evaluating DBSCAN, which Python library provides functionality to compute the silhouette score?
a. Numpy
b. Pandas
c. Scikit-learn
d. Scipy
Option c – Scikit-learn
What is considered the best approach to selecting MinPts and Eps parameters in DBSCAN?
a. Depends on the dataset; no fixed rule
b. MinPts = 1, Eps = 1
c. MinPts = 2, Eps = 0.5
d. MinPts = 5, Eps = 2
Option a – Depends on the dataset; no fixed rule
How does DBSCAN deal with clusters that have overlapping regions?
a. Assigns points in overlap to multiple clusters
b. Combines overlapping clusters into one
c. Labels overlapping points as noise
d. Excludes overlapping points from clustering
Option a – Assigns points in overlap to multiple clusters
What category of clustering does DBSCAN belong to?
a. Supervised clustering
b. Semi-supervised clustering
c. Unsupervised clustering
d. Reinforcement clustering
Option c – Unsupervised clustering
Which statement about DBSCAN is accurate?
a. It is sensitive to the order of input data
b. It can detect clusters of any shape
c. It needs the number of clusters predefined
d. It is faster than K-means clustering
Option b – It can detect clusters of any shape
Which technique is appropriate for assessing how well a time series forecasting model performs?
a. Mean Absolute Error (MAE)
b. Mean Squared Error (MSE)
c. Root Mean Squared Error (RMSE)
d. All the above methods
Option d – All the above methods
Which metric is best suited for evaluating a model that predicts event probabilities?
a. Mean Absolute Error (MAE)
b. Logarithmic loss (Log loss)
c. F1 Score
d. Coefficient of determination (R-squared)
Option b – Logarithmic loss (Log loss)
What is a suitable evaluation approach for models that output probabilities across multiple categories?
a. Mean Squared Error (MSE)
b. Logarithmic loss (Log Loss)
c. ROC Curve (Receiver Operating Characteristic)
d. Confusion Matrix
Option b – Logarithmic loss (Log loss)
Which metric helps in judging the quality of clustering by unsupervised learning algorithms?
a. F1 Score
b. Silhouette Score
c. ROC AUC Score
d. Root Mean Squared Error (RMSE)
Option b – Silhouette Score
For evaluating recommendation systems that suggest items, which method is typically used?
a. Mean Absolute Error (MAE)
b. Root Mean Squared Error (RMSE)
c. Precision
d. Coefficient of determination (R-squared)
Option b – Root Mean Squared Error (RMSE)
What evaluation method is appropriate for models analyzing textual data, such as for sentiment classification?
a. F1 Score
b. BLEU Score
c. Accuracy Score
d. Logarithmic loss (Log Loss)
Option b – BLEU Score
Which metric is used to evaluate regression models, for example, those predicting housing prices?
a. F1 Score
b. Coefficient of determination (R-squared)
c. Confusion Matrix
d. ROC Curve (Receiver Operating Characteristic)
Option b – Coefficient of determination (R-squared)
What evaluation method is suitable for models assigning probabilities to multiple classes?
a. Logarithmic loss (Log Loss)
b. Mean Squared Error (MSE)
c. F1 Score
d. Precision-Recall Curve
Option a – Logarithmic loss (Log Loss)
How can the effectiveness of a classification model be measured in identifying the negative class correctly?
a. Mean Absolute Error (MAE)
b. ROC Curve (Receiver Operating Characteristic)
c. Precision
d. Coefficient of determination (R-squared)
Option c – Precision
Which evaluation approach works well for models predicting continuous outcomes bounded between 0 and 1?
a. F1 Score
b. Coefficient of determination (R-squared)
c. Logarithmic loss (Log Loss)
d. ROC Curve (Receiver Operating Characteristic)
Option c – Logarithmic loss (Log Loss)
Which metric is appropriate for assessing models that categorize text documents?
a. Mean Absolute Error (MAE)
b. F1 Score
c. Accuracy Score
d. BLEU Score
Option c – Accuracy Score
In recommender systems, what does the recall metric indicate?
a. The portion of recommended items that are actually relevant to the user
b. The portion of all relevant items that have been recommended to the user
c. The portion of users who gave a high rating to an item
d. The portion of users who ignored a recommended item
Option a – The portion of recommended items that are actually relevant to the user
What is the main function of the popularity-based method in recommendation systems?
a. To suggest widely liked items to every user
b. To suggest items based on user demographic data
c. To suggest items similar in content to what the user likes
d. To suggest items tailored to individual user preferences
Option a – To suggest widely liked items to every user
Which method is typically applied to handle the cold start issue in recommender systems?
a. Collaborative filtering
b. Content-based filtering
c. Popularity-based filtering
d. Demographic filtering
Option d – Demographic filtering
How is the collaborative filtering memory-based method best described?
a. It applies machine learning techniques to forecast user tastes
b. It organizes user-item ratings in a matrix and recommends based on similarity among users or items
c. It recommends items by comparing their content features
d. It promotes popular items to all users indiscriminately
Option b – It organizes user-item ratings in a matrix and recommends based on similarity among users or items
What defines the collaborative filtering model-based approach?
a. It employs machine learning models to predict user preferences
b. It uses a ratings matrix to find similarity and recommend accordingly
c. It suggests items based on content similarity
d. It offers popular items to every user
Option a – It employs machine learning models to predict user preferences
Which algorithm is widely used for collaborative filtering in recommendation engines?
a. Linear Regression
b. K-means clustering
c. Decision Trees
d. Matrix factorization
Option d – Matrix factorization
Why are evaluation metrics important in recommender systems?
a. To determine how accurate the recommendations are
b. To evaluate how popular recommended items are
c. To assess the variety within recommendations
d. To gauge how satisfied users are with the recommendations
Option a – To determine how accurate the recommendations are
Which metric helps in evaluating the diversity of recommendations?
a. Precision
b. Recall
c. F1 Score
d. Novelty
Option d – Novelty
What purpose does cross-validation serve in recommender systems?
a. To divide data into training and test sets
b. To test how well the recommender system performs
c. To check how novel the recommendations are
d. To manage missing entries in the user-item rating matrix
Option b – To test how well the recommender system performs
When is it suitable to apply the popularity-based approach in recommender systems?
a. When there is no prior knowledge of user preferences
b. When personalized recommendations are required
c. When suggesting items similar to what the user likes
d. When demographic data guides the recommendations
Option b – When personalized recommendations are required
What is the main function of the item-based collaborative filtering technique?
a. Suggest items similar to those a user has interacted with.
b. Recommend items based on user demographic details.
c. Forecast how a user might rate certain items.
d. Find users with similar tastes for recommendation purposes.
Option a – Suggest items similar to those a user has interacted with.
What does the Slope One algorithm primarily do?
a. Suggest similar items to users.
b. Estimate user ratings for different items.
c. Manage missing values in the user-item data matrix.
d. Evaluate how varied the recommendations are.
Option a – Suggest similar items to users.
What role does the Association Rules algorithm play in recommender systems?
a. Suggest similar items to a user.
b. Predict the ratings a user may give.
c. Fill in missing data points in user-item matrices.
d. Discover behavioral patterns to improve recommendations.
Option b – Predict the ratings a user may give.
What is the purpose of the SVD (Singular Value Decomposition) method in recommendation systems?
a. Suggest items that resemble those preferred by the user.
b. Predict user ratings for items.
c. Deal with incomplete user-item rating data.
d. Assess recommendation diversity.
Option d – Assess recommendation diversity.
What does the k-NN (k-Nearest Neighbors) algorithm do in recommender systems?
a. Suggest similar items to users.
b. Predict ratings for user-item pairs.
c. Handle missing data in the user-item matrix.
d. Measure how diverse the recommendations are.
Option b – Predict ratings for user-item pairs.
What is the role of Singular Value Decomposition in recommender systems?
a. Recommend items similar to those the user likes.
b. Predict ratings users may assign to items.
c. Address gaps in the user-item rating data.
d. Evaluate the range of different recommendations.
Option a – Recommend items similar to those the user likes.
Which Python package is widely used for clustering algorithm implementations?
a. Pandas
b. NumPy
c. Scikit-learn
d. Matplotlib
Option c – Scikit-learn
In Python’s sklearn.cluster module, which function initiates K-Means clustering?
a. KMeans.fit()
b. sklearn.kmeans()
c. sklearn.cluster.KMeans()
d. KMeans.cluster()
Option c – sklearn.cluster.KMeans()
What parameter defines the number of clusters in K-Means clustering?
a. clusters
b. n_clusters
c. k_value
d. n_centroids
Option b – n_clusters
After applying K-Means clustering with Scikit-learn, how do you retrieve the labels assigned to clusters?
a. model.centroids
b. model.labels_
c. model.clusters_
d. model.predictions_
Option b – model.labels_
Which clustering algorithm in Scikit-learn does not require specifying the number of clusters upfront?
a. K-Means
b. DBSCAN
c. Agglomerative Clustering
d. Gaussian Mixture Model (GMM)
Option b – DBSCAN
Which metric is used in K-Means clustering to optimize the placement of centroids?
a. Silhouette Score
b. Inertia
c. Homogeneity Score
d. Adjusted Rand Index
Option b – Inertia
In scikit-learn, what is the primary function used to apply the K-Means clustering algorithm?
a. apply_kmeans()
b. fit_kmeans()
c. cluster.KMeans()
d. KMeans.fit()
Option d – KMeans.fit()
What parameter is crucial in K-Means clustering to determine the number of clusters?
a. clusters
b. n_clusters
c. k
d. num_clusters
Option b – n_clusters
Which method is used to predict cluster labels in K-Means after fitting the model?
a. predict_labels()
b. cluster_labels()
c. predict()
d. get_cluster_predictions()
Option c – predict()
What does the inertia attribute in K-Means represent?
a. Number of iterations
b. Distance between clusters
c. Sum of squared distances of samples to their closest cluster center
d. Number of clusters
Option c – Sum of squared distances of samples to their closest cluster center
Which Python library function is used to implement the DBSCAN clustering algorithm?
a. cluster.DBSCAN()
b. apply_dbscan()
c. fit_dbscan()
d. DBSCAN.fit()
Option a – cluster.DBSCAN()
Which Python library is commonly used for hierarchical clustering?
a. SciPy
b. Scikit-learn
c. StatsModels
d. NetworkX
Option a – SciPy
In hierarchical clustering using SciPy, what function is used to perform hierarchical/agglomerative clustering?
a. hierarchical()
b. agg_clustering()
c. cluster.hierarchy.linkage()
d. cluster.hierarchy.cluster()
Option c – cluster.hierarchy.linkage()
What does the method parameter control in hierarchical clustering using SciPy?
a. Number of clusters
b. Metric for calculating linkage
c. Number of iterations
d. Clustering method (single, complete, average, etc.)
Option d – Clustering method (single, complete, average, etc.)
Which function is used to visualize the dendrogram in hierarchical clustering with SciPy?
a. plot_dendrogram()
b. visualize_dendrogram()
c. dendrogram()
d. show_dendrogram()
Option c – dendrogram()
Which library is commonly used for implementing the Mean Shift clustering algorithm in Python?
a. Scikit-learn
b. SciPy
c. StatsModels
d. OpenCV
Option a – Scikit-learn
What does the bandwidth parameter control in Mean Shift clustering?
a. Number of clusters
b. Width of the kernel
c. Learning rate
d. Convergence threshold
Option b – Width of the kernel
Which library provides the AffinityPropagation class for implementing the Affinity Propagation clustering algorithm?
a. Scikit-learn
b. SciPy
c. StatsModels
d. TensorFlow
Option a – Scikit-learn
How is unsupervised learning best described?
a. A machine learning approach that categorizes input data into predefined labels
b. A method that relies on labeled datasets to forecast outcomes
c. A learning technique that identifies patterns without using labeled data
d. A strategy focused on predicting numerical outcomes
Option c – A learning technique that identifies patterns without using labeled data
Which task is typically solved using unsupervised learning?
a. Sorting images into known categories
b. Detecting suspicious financial transactions
c. Converting speech to text
d. Discovering patterns or groupings in data
Option b – Detecting suspicious financial transactions
What is the purpose of clustering in unsupervised learning?
a. Grouping similar observations without predefined categories
b. Assigning data to specific, known labels
c. Finding unusual or rare observations in data
d. Estimating continuous numeric values based on input features
Option a – Grouping similar observations without predefined categories
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