Python Machine Learning - Code Examples
- Dealing with missing data
- Identifying missing values in tabular data
- Eliminating training examples or features with missing values
- Imputing missing values
- Understanding the scikit-learn estimator API
- Handling categorical data
- Nominal and ordinal features
- Creating an example dataset
- Mapping ordinal features
- Encoding class labels
- Performing one-hot encoding on nominal features
- Partitioning a dataset into separate training and test sets
- Bringing features onto the same scale
- Selecting meaningful features
- L1 and L2 regularization as penalties against model complexity
- A geometric interpretation of L2 regularization
- Sparse solutions with L1 regularization
- Sequential feature selection algorithms
- Assessing feature importance with random forests
- Summary
Please refer to the README.md file in ../ch01 for more information about running the code examples.