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