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Classification Data Mining

Quiz by Muhammad Afif

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15 questions
Show answers
  • Q1
    Which algorithm is commonly used for classification data mining?
    DBSCAN
    Decision Tree
    Mean Shift
    K-means
    Apriori
    60s
  • Q2
    What is the purpose of training and testing data in classification data mining?
    To clean the data
    To evaluate the performance of a classification model
    To visualize the data distribution
    To label the data instances
    60s
  • Q3
    What is the accuracy of a classification model?
    The number of features used
    The time taken for model training
    The total number of data instances
    The percentage of correctly predicted instances
    60s
  • Q4
    What is the purpose of the confusion matrix in classification data mining?
    To evaluate the performance of a classification model
    To visualize the data distribution
    To preprocess the data
    To select the optimal features
    60s
  • Q5
    What is the concept of precision in classification evaluation?
    The degree to which a model is robust against overfitting.
    The proportion of true positive predictions out of all positive predictions made by a model.
    The ability of a model to correctly identify negative instances.
    The proportion of correctly classified instances out of all instances in a dataset.
    60s
  • Q6
    What is classification in Data Mining?
    A technique to categorize and assign labels to data instances based on their characteristics.
    A process of converting data into a visual representation.
    An algorithm to calculate the mean of a dataset.
    A method to identify outliers in a dataset.
    60s
  • Q7
    What is the purpose of classification in data mining?
    To calculate statistical measures of a dataset.
    To predict and assign labels to new, unlabeled data based on previous labeled data.
    To visualize data patterns and relationships.
    To identify anomalies or outliers in a dataset.
    60s
  • Q8
    What is the concept of overfitting in classification?
    When a model becomes too complex and performs well on training data but poorly on new, unseen data.
    When a model accurately predicts the labels for new, unseen data.
    When a model assigns incorrect labels to data instances.
    When a model is too simple and fails to capture the underlying patterns in data.
    60s
  • Q9
    Why is training data important in Classification in Data Mining?
    It helps in evaluating the model's performance
    It helps in visualizing data patterns
    It helps in preprocessing the data
    It helps in learning patterns and relationships to make accurate predictions
    60s
  • Q10
    What is the purpose of feature selection in Classification in Data Mining?
    To visualize the data patterns
    To preprocess the data before classification
    To evaluate the performance of the model
    To identify the most relevant features for accurate classification
    60s
  • Q11
    What is the main goal of classification in Data Mining?
    To predict the class or category of new, unseen instances
    To preprocess the data for visualization
    To analyze the patterns in the data
    To evaluate the performance of the model
    60s
  • Q12
    Which technique is used to handle missing values in Classification in Data Mining?
    Outlier Detection
    Feature Scaling
    Dimensionality Reduction
    Data Imputation
    60s
  • Q13
    What is the impact on the accuracy of a Classification model in Data Mining if there are a high number of False Positives?
    The accuracy decreases
    The accuracy increases
    The accuracy remains the same
    The impact cannot be determined
    60s
  • Q14
    Can accuracy be relied upon as the only measure to evaluate a Classification model in Data Mining?
    Yes
    No
    Depends on the dataset
    Depends on the model
    60s
  • Q15
    What is the recall-accuracy trade-off in data mining?
    The balance between maximizing recall and minimizing overall accuracy
    The balance between minimizing recall and maximizing overall accuracy
    The balance between maximizing recall and maximizing overall accuracy
    The balance between maximizing overall accuracy and minimizing precision
    60s

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