Building a predictive model for a product metric during the statistics/ML round.

Walk me through how you would build a model for this business case, starting from defining the target and features through evaluation and iteration. Write down the mathematical form of the logistic function and explain why it is appropriate for binary classification problems. In Random Forests, what exactly is "random" and why does that randomness improve model performance?

Discuss variable definition, data preprocessing, logistic equation (σ(z)=1/(1+e^{-z})), bootstrapped samples and random feature subsets in RF.

Snapchat Data Scientist interview question: Build Predictive Model for Product Metric: Steps Explained. {"blocks": [{"key": "8f023dcb", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {...