6. Dataset transformations

• 6.1. Pipelines and composite estimators
  • 6.1.2. Transforming target in regression
  • 6.1.4. ColumnTransformer for heterogeneous data
• • 6.2.1. Loading features from dicts
  • 6.2.3. Text feature extraction
• 6.3. Preprocessing data
  • 6.3.2. Non-linear transformation
  • 6.3.4. Encoding categorical features
  • 6.3.7. Generating polynomial features
• 6.4. Imputation of missing values
  • 6.4.2. Univariate feature imputation
  • 6.4.4. References
  • 6.4.6. Marking imputed values
• • 6.5.1. PCA: principal component analysis
  • 6.5.3. Feature agglomeration
• • 6.6.1. The Johnson-Lindenstrauss lemma
• 6.7. Kernel Approximation
  • 6.7.2. Radial Basis Function Kernel
  • 6.7.4. Skewed Chi Squared Kernel
• 6.8. Pairwise metrics, Affinities and Kernels
  • 6.8.2. Linear kernel
  • 6.8.4. Sigmoid kernel
  • 6.8.6. Laplacian kernel
• 6.9. Transforming the prediction target ()