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Answer: Principal Component Analysis (PCA), a technique designed to transform a large set of variables into a smaller one that still contains most of the information in the large set.
**Correct Option: C. Principal Component Analysis (PCA)** **Explanation:** Principal Component Analysis (PCA) is specifically designed for dimensionality reduction. It works by transforming the original variables into a new set of variables, the principal components, which are orthogonal (uncorrelated) and capture the maximum variance in the data. This makes PCA the most suitable option for the given scenario because: - It directly addresses the need to reduce dimensionality while preserving variability. - It helps in identifying patterns in data based on the correlation between features. - It facilitates data visualization by reducing the data to two or three principal components. **Why other options are incorrect:** - **A. Gradient Descent:** While useful for optimizing model parameters, it does not inherently reduce the number of features in the dataset. - **B. Random Forest:** Although it can indicate feature importance, it does not reduce the dimensionality of the dataset in a way that preserves the original variability or facilitates visualization. - **D. K-Nearest Neighbors (KNN):** This algorithm does not reduce dimensionality; it uses all features to compute distances between data points.
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In the context of preparing a large dataset for machine learning, you are tasked with reducing its dimensionality to improve model performance and reduce computational costs. The dataset contains hundreds of features, some of which are highly correlated. Given the constraints of needing to preserve as much of the original variability as possible and the requirement to facilitate easier data visualization, which of the following techniques would be the MOST appropriate to achieve these goals? Choose one correct option.
A
Gradient Descent, as it optimizes the model parameters to minimize the loss function, indirectly reducing dimensionality by focusing on the most relevant features.
B
Random Forest, which can inherently select important features during the construction of decision trees, thus reducing dimensionality.
C
Principal Component Analysis (PCA), a technique designed to transform a large set of variables into a smaller one that still contains most of the information in the large set.
D
K-Nearest Neighbors (KNN), which reduces dimensionality by considering only the closest neighbors in the feature space for making predictions.