6. Dataset transformations

• • 6.1.1. Pipeline: chaining estimators
  • 6.1.3. FeatureUnion: composite feature spaces
• 6.2. Feature extraction
  • 6.2.2. Feature hashing
  • 6.2.4. Image feature extraction
• • 6.3.1. Standardization, or mean removal and variance scaling
  • 6.3.3. Normalization
  • 6.3.5. Discretization
  • 6.3.8. Custom transformers
• • 6.4.1. Univariate vs. Multivariate Imputation
  • 6.4.3. Multivariate feature imputation
  • 6.4.5. Nearest neighbors imputation
• 6.5. Unsupervised dimensionality reduction
  • 6.5.2. Random projections
• 6.6. Random Projection
  • 6.6.2. Gaussian random projection
• • 6.7.1. Nystroem Method for Kernel Approximation
  • 6.7.3. Additive Chi Squared Kernel
  • 6.7.5. Mathematical Details
• • 6.8.1. Cosine similarity
  • 6.8.3. Polynomial kernel
  • 6.8.5. RBF kernel
  • 6.8.7. Chi-squared kernel
• • 6.9.1. Label binarization