Improving Model Accuracy

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)