You have a dataset with numerous features, and you suspect that many of them are correlated. Using which technique can you both reduce the dimensionality and tackle multicollinearity?
- Data Imputation
- Decision Trees
- Feature Scaling
- Principal Component Analysis (PCA)
Principal Component Analysis (PCA) can reduce dimensionality by transforming correlated features into a smaller set of uncorrelated variables. It addresses multicollinearity by creating new axes (principal components) where the original variables are no longer correlated, thus improving the model's stability and interpretability.
Loading...
Related Quiz
- Support Vector Machines (SVM) aim to find a ______ that best divides a dataset into classes.
- A researcher is working with a large dataset of patient medical records with numerous features. They want to visualize the data in 2D to spot any potential patterns or groupings but without necessarily clustering the data. Which technique would they most likely employ?
- You're working with a large dataset of facial images. You want to reduce the dimensionality of the images while preserving their primary features for facial recognition. Which neural network structure would you employ?
- Which of the following best describes the dilemma faced in the multi-armed bandit problem?
- When aiming to reduce both bias and variance, one might use techniques like ________ to regularize a model.