How does PCA help in reducing the dimensionality of the dataset?
- By creating new uncorrelated variables
- By grouping similar data together
- By removing unnecessary data
- By rotating the data to align with axes
PCA reduces the dimensionality of a dataset by creating new uncorrelated variables that successively maximize variance. These new variables or "principal components" can replace the original variables, thus reducing the data's dimensionality.
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Related Quiz
- In PCA, if two variables are similar, they will have _______ loadings on the same component.
- The Mann-Whitney U test assumes that the samples are ________ and ________.
- __________ in multiple linear regression refers to the proportion of the variance in the dependent variable that is predictable from the independent variables.
- A Variance Inflation Factor (VIF) greater than 5 indicates a high degree of _______ among the predictors.
- In the Mann-Whitney U test, what does a lower U value indicate?