Podcast
Questions and Answers
What is the key difference between PCA and Factor Analysis (FA) in terms of the number of axes?
What is the key difference between PCA and Factor Analysis (FA) in terms of the number of axes?
- The number of axes in PCA is equal to the number of variables, while in FA it is limited to a few factors. (correct)
- Both PCA and FA have the same number of axes.
- The number of axes in FA is equal to the number of variables, while in PCA it is limited to a few factors.
- PCA has no limit on the number of axes, while FA is limited to a few factors.
What is the purpose of data discretization?
What is the purpose of data discretization?
- To group numerical data into categories for analysis. (correct)
- To confuse the analysis by making data less understandable.
- To increase the variance in the dataset.
- To convert categorical data into numerical data.
In equal width binning, how is the data sorted for grouping?
In equal width binning, how is the data sorted for grouping?
- From smallest to largest. (correct)
- From largest to smallest.
- No specific sorting method.
- Based on random selection.
What does equal depth binning ensure?
What does equal depth binning ensure?
What is one common issue caused by outliers in data analysis?
What is one common issue caused by outliers in data analysis?
How does equal width binning handle skewed data?
How does equal width binning handle skewed data?
What is the purpose of checking if scales are similar for columns with measurements?
What is the purpose of checking if scales are similar for columns with measurements?
What is the main objective of Model Planning in the software for Data Pre-Processing?
What is the main objective of Model Planning in the software for Data Pre-Processing?
What is the function of the testing set in Model Building phase?
What is the function of the testing set in Model Building phase?
In geospatial datasets, why is it important to check if abbreviations of locations are consistent?
In geospatial datasets, why is it important to check if abbreviations of locations are consistent?
What differentiates the testing set from the training set in Model Building phase?
What differentiates the testing set from the training set in Model Building phase?
What role does Model Building phase play in developing datasets for production?
What role does Model Building phase play in developing datasets for production?
What is the percentage of errors in the predictions?
What is the percentage of errors in the predictions?
Which attribute was identified as having the best ability to increase group homogeneity?
Which attribute was identified as having the best ability to increase group homogeneity?
What percentage of rows remain after removing 37 from a total of 600?
What percentage of rows remain after removing 37 from a total of 600?
What is the likelihood of an individual saying 'yes' in the group with income greater than $51,284.3?
What is the likelihood of an individual saying 'yes' in the group with income greater than $51,284.3?
When using 'Region' as the attribute for splitting, what percentage of rows are involved?
When using 'Region' as the attribute for splitting, what percentage of rows are involved?
How many rows are left after considering 'Age' as an attribute?
How many rows are left after considering 'Age' as an attribute?
What is the main disadvantage of increasing the number of epochs to an infinite number?
What is the main disadvantage of increasing the number of epochs to an infinite number?
In what scenario is SVM preferred over ANN?
In what scenario is SVM preferred over ANN?
What transformation is needed to move from a linear to a nonlinear boundary in SVM?
What transformation is needed to move from a linear to a nonlinear boundary in SVM?
How do kernel methods help in SVM?
How do kernel methods help in SVM?
Why are ensemble classification techniques considered better than decision trees?
Why are ensemble classification techniques considered better than decision trees?
How is the relationship between soloist and orchestra analogous to the relationship between decision trees and ensembles?
How is the relationship between soloist and orchestra analogous to the relationship between decision trees and ensembles?
What is the main purpose of a perceptron in classification?
What is the main purpose of a perceptron in classification?
In the context of support vector machines, what do support vectors represent?
In the context of support vector machines, what do support vectors represent?
What is a common characteristic of an invalid line in classification using a perceptron?
What is a common characteristic of an invalid line in classification using a perceptron?
Why are the input values normalized before being input into the perceptron for classification?
Why are the input values normalized before being input into the perceptron for classification?
What happens when a data point has a negative number output after being input into the perceptron?
What happens when a data point has a negative number output after being input into the perceptron?
How is a perceptron line used to classify data points?
How is a perceptron line used to classify data points?
