Interview Practice Questions

What important aspects should be considered when developing ML system?

How to compare quality of different annotations and labeling?

How to tune number of leaves in trees to avoid overfitting?

Advantages of simple models for forecasting and control?

When to conduct A/B testing of ML models?

How to model customer response to marketing offers?

How can model account for differences between different product offerings?

How to generally detect model overfitting?

How to handle last two columns with problematic data?

How to find anomalies in salary structure using ML?

How to interpret 24-point drop in model metrics?

How to work with inherited ML solutions from previous teams?

What is Gini coefficient in credit scoring context?

What types of histograms are useful for ML data analysis?

Why can Gini coefficient be inverted when calculated through AUC?

Alternatives to median imputation for missing values in ML?

How to handle fraud features that exist only for suspicious customers?

What minimum metric change is considered significant in ML?

How many positive and negative examples needed for model training?

How to understand that there's too little data to build reliable model?