Which of the following is an example of a dimensionality reduction technique?Question 6Answera.Support vector machine (SVM)b.Principal component analysis (PCA)c.AdaBoostd.K-nearest neighbors (KNN)
Question
Which of the following is an example of a dimensionality reduction technique?Question 6Answera.Support vector machine (SVM)b.Principal component analysis (PCA)c.AdaBoostd.K-nearest neighbors (KNN)
Solution
The example of a dimensionality reduction technique among the options given is b. Principal Component Analysis (PCA).
Here's why:
a. Support Vector Machine (SVM) - This is a type of supervised machine learning algorithm used for classification or regression problems. It does not reduce dimensionality.
b. Principal Component Analysis (PCA) - This is a technique used to reduce the dimensionality of datasets, increasing interpretability but at the same time minimizing information loss. It does this by creating new uncorrelated variables that successively maximize variance.
c. AdaBoost - This is a boosting algorithm, generally used to improve the performance of decision trees on binary classification problems. It does not reduce dimensionality.
d. K-nearest neighbors (KNN) - This is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until function evaluation. It does not reduce dimensionality.
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