What is Principal Component Analysis?
Question
What is Principal Component Analysis?
Solution
Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables.
Similar Questions
How does the term "principal component analysis" (PCA) contribute to dimensionality reduction in data analysis?By clustering similar data points togetherBy transforming features into a lower-dimensional spaceBy creating new features based on existing onesBy evaluating feature importance
Principal Component Analysis (PCA)... (Select ANY correct answer) A. can improve model interpretability. B. can improve model accuracy. C. can convert non-linear problems into linear problems. D. can reduce the number of attributes/features used for modeling.
Explain pca
In principal component analysis, the projected lower dimensional space corresponds to – A. subset of the original co-ordinate axis B. eigenvectors of the data covariance matrix C. eigenvectors of the data distance matrix D. orthogonal vectors to the original co-ordinate axis
the main uses of dimensional analysis
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