Data Pre-Processing III: Data Reduction

Questions and Answers

What is the primary goal of dimensionality reduction in a dataset?

To increase the number of input features

To decrease the accuracy of predictive modeling

To reduce the number of input features (correct)

To eliminate all input features

Which of the following is NOT a benefit of data reduction?

Reduced storage cost

Increased training time (correct)

Accuracy improvements

Improved Data Visualization

What does the term 'curse of dimensionality' refer to?

Dimensionality is not relevant in machine learning

More input features can complicate predictive modeling (correct)

Adding more dimensions always improves model performance

More input features make modeling tasks easier

What is the purpose of feature selection in machine learning?

To identify the best set of features that build useful models Signup and view all the answers

Which technique is associated with feature extraction?

Principal Component Analysis Signup and view all the answers

What characterizes a weakly relevant feature in feature selection?

It contributes little information Signup and view all the answers

What is a consequence of adding features beyond the optimal number?

Performance degradation due to added noise Signup and view all the answers

Which method is NOT a type of feature selection?

Dimensional analysis Signup and view all the answers

What is a significant drawback of the wrapper approach in feature selection?

It is computationally very expensive. Signup and view all the answers

Which method is utilized in the backward feature elimination process?

All features are selected initially, then the least useful ones are removed. Signup and view all the answers

What distinguishes embedded methods from wrapper and filter methods?

Embedded methods combine benefits of wrapper and filter methods while maintaining a reasonable computational cost. Signup and view all the answers

How does feature extraction differ from feature selection?

Feature extraction creates new features from existing ones through mapping. Signup and view all the answers

Which of the following is a method used in the wrapper approach for feature selection?

Forward Feature Selection Signup and view all the answers

What characteristic makes Age and Height redundant features?

They provide the same type of information regarding students. Signup and view all the answers

Which metric is used when performing correlation analysis to find redundant features?

Correlation coefficient (r) Signup and view all the answers

What condition classifies a distance metric as Euclidean distance?

When r = 2 Signup and view all the answers

Which of the following metrics is NOT used for binary features?

Cosine similarity Signup and view all the answers

What is the main principle behind the Filter Approach in feature selection?

Statistical measures determine the goodness of features without a learning algorithm. Signup and view all the answers

Which distance metric is specifically defined as the number of differing values in two feature vectors?

Hamming distance Signup and view all the answers

Which of the following approaches employs learning algorithms to evaluate feature subsets?

Wrapper Approach Signup and view all the answers

What does the Jaccard Similarity measure in relation to two sets?

The ratio of shared elements to all elements present in both sets. Signup and view all the answers

Study Notes

Dimensionality and Data Reduction

Dimensionality refers to the number of input variables or features in a dataset.
Dimensionality reduction techniques aim to reduce the number of input variables to simplify modeling tasks.
The "curse of dimensionality" implies that more features can make predictive modeling more difficult.
There exists an optimal number of features for effective machine learning tasks; excess features lead to performance degradation due to noise.

Benefits of Data Reduction

Enhances accuracy of predictions.
Reduces the risk of overfitting.
Accelerates training speed.
Improves data visualization.
Increases model explainability.
Enhances storage efficiency and reduces storage costs.

Data Reduction Techniques

Feature Selection
- Involves identifying the best set of features for creating useful models.
- Focuses on maximizing relevance and minimizing redundancy among features.
Feature Extraction
- Involves creating new features from combinations of original features.
- Techniques include Principal Component Analysis (PCA) and Singular Value Decomposition (SVD).

Feature Selection

Key processes:
- Maximizing Feature Relevance
  - Strongly relevant features provide significant information.
  - Weakly relevant features contribute limited information.
  - Irrelevant features provide no useful data.
- Minimizing Feature Redundancy
  - Assessing similarity between features to eliminate redundancy.

Measuring Feature Redundancy

Redundancy assessed through correlation and distance metrics.
Correlation-based features help determine similarity, denoted by 'r'.
Distance-based metrics include:
- Minkowski distance (Euclidean for r=2; Manhattan for r=1).
- Cosine similarity for vectorized features.

Metrics for Binary Features

Hamming Distance: Counts the number of differing values in two feature vectors.
Jaccard Distance: 1 - Jaccard Similarity; evaluates feature similarity based on their values.
Simple Matching Coefficient (SMC): Measures similarity by count of matching values among features.

Feature Selection Approaches

Filter Approach
- Selects feature subsets using statistical measures without a learning model.
- Uses metrics like correlation, chi-square, and Information Gain for selection.
Wrapper Approach
- Involves training a learning model for each subset of features.
- More computationally intensive but often yields better performance.

Wrapper Approach Searching Methods

Forward Feature Selection: Iteratively adds the feature that improves model performance the most.
Backward Feature Elimination: Starts with all features, removing the least useful feature iteratively.
Exhaustive Feature Selection: Tests all possible combinations of features to find the best subset.
Embedded Approach
- Combines benefits of both filter and wrapper methods.
- Features are selected with consideration of model training in each iteration, focusing on those that contribute most.

Feature Extraction

Creates a new feature set from existing features based on a mapping function.
Transforms the original feature set into a new set while retaining essential data characteristics.

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Description

Explore the techniques of dimensionality reduction in data pre-processing. This quiz covers the challenges posed by high dimensionality and the optimal strategies to minimize input variables for improved predictive modeling. Test your understanding of the concepts and methods involved.