Sebastian Raschka, 2015

Python Machine Learning - Code Examples

Chapter 4 - Building Good Training Sets – Data Preprocessing

• Dealing with missing data
  • Eliminating samples or features with missing values
  • Imputing missing values
  • Understanding the scikit-learn estimator API
• Handling categorical data
  • Mapping ordinal features
  • Encoding class labels
  • Performing one-hot encoding on nominal features
• Partitioning a dataset in training and test sets
• Bringing features onto the same scale
• Selecting meaningful features
  • Sparse solutions with L1 regularization
  • Sequential feature selection algorithms
• Assessing feature importance with random forests
• Summary