Feature Engineering

Feature engineering is the process of using domain knowledge to create features that make machine learning algorithms work better. This section covers various techniques and best practices for feature engineering.

Data Preprocessing

Data Cleaning

• Handling Missing Values
• Removing Duplicates
• Handling Outliers
• Data Validation

Data Transformation

• Scaling

  • Min-Max Scaling
  • Standard Scaling
  • Robust Scaling
  • Normalization

• Encoding

  • One-Hot Encoding
  • Label Encoding
  • Target Encoding
  • Feature Hashing

Feature Creation

Numerical Features

• Mathematical Transformations
• Polynomial Features
• Interaction Features
• Domain-Specific Features

Text Features

• Bag of Words
• TF-IDF
• Word Embeddings
• N-grams

Temporal Features

• Time-based Features
• Lag Features
• Rolling Statistics
• Seasonal Features

Feature Selection

Filter Methods

• Correlation Analysis
• Chi-Square Test
• Information Gain
• Variance Threshold

Wrapper Methods

• Forward Selection
• Backward Elimination
• Recursive Feature Elimination
• Exhaustive Feature Selection

Embedded Methods

• Lasso Regularization
• Ridge Regularization
• Elastic Net
• Tree Importance

Dimensionality Reduction

Linear Methods

• Principal Component Analysis
• Linear Discriminant Analysis
• Factor Analysis
• Truncated SVD

Non-linear Methods

• t-SNE
• UMAP
• Kernel PCA
• Autoencoders

Advanced Techniques

Automated Feature Engineering

• Feature Tools
• Deep Feature Synthesis
• AutoML Feature Engineering
• Feature Learning

Domain-Specific Features

• Image Features
• Audio Features
• Geographic Features
• Network Features

Best Practices

Feature Selection Pipeline

• Feature Importance Analysis
• Feature Selection Strategy
• Cross-Validation
• Feature Store

Production Considerations

• Scalability
• Real-time Feature Engineering
• Feature Monitoring
• Version Control