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Questions and Answers
What is a primary goal of data preparation in data mining and machine learning?
What is a primary goal of data preparation in data mining and machine learning?
How does data cleaning improve the quality of data?
How does data cleaning improve the quality of data?
Which algorithm predicts missing values by averaging from nearest neighbors?
Which algorithm predicts missing values by averaging from nearest neighbors?
Which of the following is NOT considered a factor of data quality?
Which of the following is NOT considered a factor of data quality?
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What technique is used to reduce the noise in a dataset?
What technique is used to reduce the noise in a dataset?
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What is the purpose of data reduction in data preparation?
What is the purpose of data reduction in data preparation?
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What does median imputation primarily maintain in a dataset?
What does median imputation primarily maintain in a dataset?
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Why is it essential to ensure compatibility of data from different sources?
Why is it essential to ensure compatibility of data from different sources?
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What is the definition of an outlier in a dataset?
What is the definition of an outlier in a dataset?
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What is the primary purpose of using the KNN method for imputation?
What is the primary purpose of using the KNN method for imputation?
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What method can be used to handle missing values among attributes in a dataset?
What method can be used to handle missing values among attributes in a dataset?
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What can be the impact of not preprocessing data effectively?
What can be the impact of not preprocessing data effectively?
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What are common sources of noise in data collection?
What are common sources of noise in data collection?
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When training a decision tree, how does it handle instances with missing values?
When training a decision tree, how does it handle instances with missing values?
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Which of the following is an outcome of poor data quality?
Which of the following is an outcome of poor data quality?
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Which of the following methods is NOT a technique used for smoothing data?
Which of the following methods is NOT a technique used for smoothing data?
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What is the primary disadvantage of ignoring tuples with missing class labels?
What is the primary disadvantage of ignoring tuples with missing class labels?
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Why is data imputation utilized in data processing?
Why is data imputation utilized in data processing?
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Which imputation method is most appropriate for numerical data that is skewed?
Which imputation method is most appropriate for numerical data that is skewed?
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What is the key characteristic of mode imputation?
What is the key characteristic of mode imputation?
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Under what circumstances is fixed value imputation particularly useful?
Under what circumstances is fixed value imputation particularly useful?
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Which imputation technique is appropriate for time-series data?
Which imputation technique is appropriate for time-series data?
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What is the best practice when using the mean imputation method?
What is the best practice when using the mean imputation method?
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What is a significant drawback of using the imputation method?
What is a significant drawback of using the imputation method?
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Which technique involves testing subsets of features to find the optimal combination?
Which technique involves testing subsets of features to find the optimal combination?
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What is the primary purpose of data transformation in data analysis?
What is the primary purpose of data transformation in data analysis?
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What is the outcome of normalization (Min-Max Scaling)?
What is the outcome of normalization (Min-Max Scaling)?
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Which of the following is an example of an embedded method?
Which of the following is an example of an embedded method?
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Which sampling method involves selecting entire groups rather than individual cases?
Which sampling method involves selecting entire groups rather than individual cases?
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What does standardization (z-score normalization) achieve?
What does standardization (z-score normalization) achieve?
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Which feature selection method evaluates different models to determine the best features?
Which feature selection method evaluates different models to determine the best features?
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Which of the following is true about chi-square tests?
Which of the following is true about chi-square tests?
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What is the primary goal of dimensionality reduction?
What is the primary goal of dimensionality reduction?
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Which technique is primarily used in dimensionality reduction to identify directions of maximum variance?
Which technique is primarily used in dimensionality reduction to identify directions of maximum variance?
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What characteristic is unique to t-SNE compared to PCA in dimensionality reduction?
What characteristic is unique to t-SNE compared to PCA in dimensionality reduction?
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Which of the following techniques is NOT associated with feature selection?
Which of the following techniques is NOT associated with feature selection?
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What step must be performed first in the PCA process?
What step must be performed first in the PCA process?
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How does feature selection enhance model performance?
How does feature selection enhance model performance?
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Which statistical method helps to identify the contribution of features to a target variable?
Which statistical method helps to identify the contribution of features to a target variable?
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What is one of the benefits of using dimensionality reduction in data analysis?
What is one of the benefits of using dimensionality reduction in data analysis?
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What is a primary use of the interquartile range (IQR) in data analysis?
What is a primary use of the interquartile range (IQR) in data analysis?
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How does the Isolation Forest algorithm identify outliers?
How does the Isolation Forest algorithm identify outliers?
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Which statement about clustering in the context of noise removal is false?
Which statement about clustering in the context of noise removal is false?
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What is the purpose of data reduction in data analysis?
What is the purpose of data reduction in data analysis?
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When using the IQR method, what does a negative price value indicate?
When using the IQR method, what does a negative price value indicate?
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Which clustering algorithm is commonly mentioned as a method for detecting noise?
Which clustering algorithm is commonly mentioned as a method for detecting noise?
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Which of the following is a correct method to calculate the lower bound for outlier detection using the IQR?
Which of the following is a correct method to calculate the lower bound for outlier detection using the IQR?
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What is a common characteristic of outliers in clustered data?
What is a common characteristic of outliers in clustered data?
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Study Notes
Data Mining and Practical Machine Learning (ICT 462-3) - Week 2: Data Preparation Techniques
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Data preparation is critical for data mining and machine learning
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Raw data needs transformation for effective analysis and modeling
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Data quality directly impacts model performance
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Data preprocessing aims to improve data quality, increase model efficiency, and ensure data compatibility
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Data quality factors include accuracy, completeness, consistency, timeliness, believability, and interpretability
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Data preparation includes data cleaning, data integration, data transformation, and data reduction
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Data cleaning routines "clean" the data by:
- Filling in missing values
- Smoothing noisy data
- Identifying or removing outliers
- Resolving inconsistencies
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Techniques for handling missing values:
- Ignore the tuple (with caution)
- Imputation (replacing missing values):
- Mean imputation
- Median imputation
- Mode imputation
- Fixed value imputation
- Next or Previous Value
- Maximum or Minimum Value
- Missing Value Prediction (using machine learning models like KNN or random forest)
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Handling noisy data:
- Noise is random error or variance in measured variables
- Sources of noise include measurement errors and processing errors
- Outliers are data points that significantly deviate from the rest of the data
- Smoothing techniques reduce noise
- Binning (dividing data into intervals and replacing values within bins with representative values like mean or median)
- Bin mean method
- Bin median method
- Bin boundary method
- Regression methods
- Filtering methods (like median filtering or low-pass filtering
- Binning (dividing data into intervals and replacing values within bins with representative values like mean or median)
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Methods for outlier detection:
- Z-score (identifies points far from the mean)
- IQR (interquartile range)
- Isolation Forest (isolates data points in a decision tree)
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Clustering can help in noise removal by grouping similar data points
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Several clustering algorithms can detect and eliminate noisy data points
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Points far from cluster centroids or belonging to small, isolated clusters are considered outliers
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Data reduction methods:
- Dimensionality reduction (e.g., PCA, t-SNE): Reduces features while preserving essential information
- Feature selection: Retains relevant features, discarding irrelevant or redundant ones (e.g., statistical methods, wrapper methods, embedded methods)
- Sampling: Represents the large data set using a small random sample
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Data transformation methods:
- Normalization (min-max scaling): Rescales data to a specific range (e.g., 0-1)
- Standardization (z-score normalization): Transforms data to have a zero mean and unit variance
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Data discretization: Converting numeric attributes into interval or conceptual labels (e.g., age could be '0-10', '11-20', etc.)
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Methods for discretization:
- Binning
- Histogram Analysis
- Discretization by Cluster
- Correlation methods
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Encoding categorical variables: Converting categorical (non-numeric) data into a numerical form - One-Hot Encoding - Label Encoding
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Description
Explore the essential techniques for data preparation in data mining and machine learning. This quiz covers the importance of data quality and the various methods for data cleaning, integration, transformation, and reduction. Understand how these processes impact model performance and analysis.