by chandan singh

a checklist of things that may improve your ml model performance

data splitting

• is there any dependence within data splits (e.g. temporal correlations) that would artificially affect your accuracy estimates?

visualizing data

• look at histograms of outcomes / key features
• see if features can be easily reduced to lower dimensions (e.g. pca, lda)

preprocessing

• normalize features and output
• balance the data (e.g., random sampling, random sampling + ensembling, smote)
• do feature selection with simple screening (e.g. variance, correlation)
• do feature selection using a model (e.g. tree, lasso)

debugging

• can the model achieve zero training error on a single example?
• how do simple baselines (e.g. linear models, decision trees) perform?

feature engineering

• visualize the outputs of dim reduction / transformations (e.g. pca, sparse coding, nmf) on your features
• for correlated features, group them together or extract out a more stable feature

modeling

• try simple rules to cover most of the cases