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9.1: Study Guide

  • Page ID
    141905
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    The following are meant to guide your exploration and discussion of chapter 9 (Gradient Boosting):

    1. Explain how Gradient Boosting builds models using weak learners and sequential learning.
    2. Distinguish Gradient Boosting from bagging and stacking methods.
    3. Identify and adjust key hyperparameters to optimize performance.
    4. Outline the step-by-step Gradient Boosting algorithm.
    5. Build and evaluate models for business problems using relevant metrics.
    6. Interpret model results with feature importance, partial dependence plots (PDPs), and SHapley additive explanations (SHAP) values.
    7. Present model insights in clear, actionable business terms.

    This page titled 9.1: Study Guide is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Elbert L. Hearon, M.B.A., M.S..

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