Podcast
Questions and Answers
Which method replaces data with a smaller representation, such as parameters using parametric methods like regression models?
Which method replaces data with a smaller representation, such as parameters using parametric methods like regression models?
- Singular Value Decomposition (SVD)
- Aggregates
- Wavelet transform
- Regression models (correct)
What technique helps obtain a compressed representation of the original data by reducing the dimensionality of a dataset?
What technique helps obtain a compressed representation of the original data by reducing the dimensionality of a dataset?
- Standardising
- Discretisation
- Normalisation
- Principal Component Analysis (PCA) (correct)
What is the main purpose of feature selection in data pre-processing?
What is the main purpose of feature selection in data pre-processing?
- To increase dimensionality
- To decrease data accuracy
- To introduce redundancy
- To eliminate redundant features (correct)
Which approach to feature selection selects features before running the data mining algorithm using an approach independent of the task?
Which approach to feature selection selects features before running the data mining algorithm using an approach independent of the task?
Which method uses the target data mining algorithm as a black box to find the best subset of attributes for feature selection?
Which method uses the target data mining algorithm as a black box to find the best subset of attributes for feature selection?
Why do data mining algorithms usually work better if the dimensionality of the data is lower?
Why do data mining algorithms usually work better if the dimensionality of the data is lower?
What is the purpose of standardisation in data pre-processing?
What is the purpose of standardisation in data pre-processing?
In data transformations, what is the main reason for applying normalisation?
In data transformations, what is the main reason for applying normalisation?
Why is it important to put variables on similar scales during data pre-processing?
Why is it important to put variables on similar scales during data pre-processing?
What is the reason for removing size effects and giving all variables equal weight during transformations?
What is the reason for removing size effects and giving all variables equal weight during transformations?
Which transformation technique helps to maintain the validity of results while making them more useful?
Which transformation technique helps to maintain the validity of results while making them more useful?
How can different measurement units impact data analysis according to the text?
How can different measurement units impact data analysis according to the text?
What is the primary purpose of transforming variables by centring?
What is the primary purpose of transforming variables by centring?
When normalising data for methods like Neural network and Clustering, what is a key reason for this transformation?
When normalising data for methods like Neural network and Clustering, what is a key reason for this transformation?
Which statement best describes why mathematical transformations are used?
Which statement best describes why mathematical transformations are used?
In the context of reasons for mathematical transformations, what does it mean to 'improve homogeneity of data'?
In the context of reasons for mathematical transformations, what does it mean to 'improve homogeneity of data'?
When considering data transformation, what should be done if it is not necessary to transform the data?
When considering data transformation, what should be done if it is not necessary to transform the data?
What is suggested as a better alternative to arbitrary and uninterpretable results when transforming data?
What is suggested as a better alternative to arbitrary and uninterpretable results when transforming data?
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