I wrote a few quick bullet points down from the article “8 Proven Ways for improving the “Accuracy” of a Machine Learning Model” for future reference.
Improving Accuracy
- Add more data
- Fix missing values
- Continuous: impute with median/mean/mode
- Categorical: treat as separate class
- Predict missing classes with k-nearest neighbours
- Outliers
- Delete
- Bin
- Impute
- Treat as separate to the others
- Feature engineering
- Transform and normalise: scale between 0-1
- Eliminate skewness (e.g. log) for algorithms that require normal distribution
- Create features: Date of transactions might not be useful but day of the week may be
- Feature selection
- Best features to use: identify via visualisation or through domain knowledge
- Significance: Use p-values and other metrics to identify the right values. Can also use dimensionally reduction while preserving relationships in the data
- Test multiple machine learning algorithms and tune their parameters
- Ensemble methods: combine multiple week predictors (bagging and boosting)
