Explore the essential differences between feature selection and feature extraction in machine learning with this engaging quiz. Enhance your understanding of dimensionality reduction techniques, use cases, and core principles to boost your data preprocessing skills.
What is the primary purpose of feature selection in data preprocessing?
Explanation: Feature selection involves identifying and retaining the most important features from the original dataset, which helps improve model performance and reduce overfitting. Creating new combinations is feature extraction, not selection. Normalization adjusts scales but does not select features. Generating random features adds noise rather than relevance.
How does feature extraction typically modify data when applied to a dataset?
Explanation: Feature extraction creates new variables by transforming or combining existing features, such as through mathematical or statistical methods. Deleting rows with missing values is data cleaning, not extraction. Dataset splitting is part of model evaluation. Random sample selection does not create new features.
If you want to reduce a dataset's number of input variables while keeping only the original columns, which technique should you use?
Explanation: Feature selection reduces dimensionality by picking a subset of the original columns, ensuring all selected variables remain as they were. Feature extraction generates new columns, not using only original ones. Data augmentation increases the dataset, and target encoding modifies categorical features based on target distributions.
After applying feature extraction like Principal Component Analysis (PCA), how are the resulting features best described?
Explanation: Feature extraction techniques like PCA create new features as mathematical combinations of original ones, often losing clear interpretability. These features are different from the initial ones. They are not always categorical, nor do they necessarily represent straightforward physical measurements.
When a dataset has several highly correlated features, which is a typical advantage of feature extraction over feature selection?
Explanation: Feature extraction methods like PCA can transform correlated features into a set of uncorrelated variables, often called principal components. Simply removing duplicates is not feature extraction's primary function. Assigning random weights is unrelated, and extraction does not always guarantee accuracy improvement.
Which technique typically preserves the interpretability of input features in the model's results?
Explanation: Feature selection keeps original features as inputs, making it easier to interpret which variables influence model outcomes. Feature extraction makes new combined features that can be hard to interpret. Image augmentation and label transformation handle data expansion and target changes, not feature interpretability.
In a facial recognition system, which technique would you use to reduce thousands of pixel values to a smaller set of meaningful numerical features?
Explanation: Feature extraction can condense high-dimensional data (like pixels) into fewer, informative features using methods such as PCA or Fourier transforms. Feature selection would only pick certain pixels, potentially missing holistic patterns. Random oversampling deals with imbalances, and one-hot encoding turns categoricals into binaries.
What is a key conceptual difference between feature selection and feature extraction?
Explanation: Feature selection keeps original columns, whereas feature extraction forms new, transformed features. Selection typically reduces the feature set, extraction can reduce or increase features. Both work with different data types, and their outputs vary by method used.
How can feature selection help reduce the risk of overfitting in machine learning models?
Explanation: Feature selection reduces overfitting by discarding features that do not contribute valuable information, which helps prevent models from learning unhelpful patterns. Increasing features or adding noise generally increases overfitting risk, while shrinking training data can hurt performance.
Which of the following is most often considered a feature extraction technique?
Explanation: PCA is a classic feature extraction method, transforming existing features into new principal components. Recursive Feature Elimination, Variance Thresholding, and Forward Selection are common feature selection techniques, not extraction, as they remove or retain existing variables instead of creating new ones.