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Identify and Fix Predictive Model Performance Gaps

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in feature engineering (especially temporal encoding), feature standardization, and handling imbalanced datasets for predictive model performance within the Machine Learning domain.

  • medium
  • Google
  • Machine Learning
  • Data Scientist

Identify and Fix Predictive Model Performance Gaps

Company: Google

Role: Data Scientist

Category: Machine Learning

Difficulty: medium

Interview Round: Technical Screen

##### Scenario Reviewing an existing predictive model for operational issues and performance gaps. ##### Question A model treats calendar month as a continuous variable. What problems can this cause and how would you fix them? Why is standardizing predictors important before fitting certain models, and what might go wrong if you skip it? Your training data are highly imbalanced. Describe two ways to adjust the loss function or evaluation metrics so recall is properly rewarded. ##### Hints Discuss cyclic features, scale sensitivity, weighted loss, focal loss, precision-recall trade-off.

Quick Answer: This question evaluates a data scientist's competency in feature engineering (especially temporal encoding), feature standardization, and handling imbalanced datasets for predictive model performance within the Machine Learning domain.

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Google
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Machine Learning
82
0

Model Review: Month Encoding, Feature Scaling, and Imbalanced Data

Context

You are auditing an existing predictive model for operational performance. The current implementation encodes calendar month as a continuous variable and is trained on imbalanced data. Address the following:

Questions

  1. Month as a continuous variable
    • What problems can arise if the model treats calendar month (e.g., Jan=1, ..., Dec=12) as a continuous feature?
    • How would you fix this encoding to capture seasonality correctly?
  2. Feature standardization
    • Why is standardizing predictors important before fitting certain models?
    • What can go wrong if you skip standardization?
  3. Imbalanced data and recall
    • Your training data are highly imbalanced. Describe two concrete ways to adjust the loss function or the evaluation/thresholding so recall is properly rewarded.

Solution

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