Review of Modeling Results

Here’s a look at a review of modeling results.

We'll cover the following

Summary of modeling activities with case study data
Feature engineering
Ensembling multiple models
Balancing classes

In order to develop a binary classification model to meet the business requirements of our client, we have now tried several modeling techniques with varying degrees of success. In the end, we'd like to choose the model with the best performance to do further analyses on and present to our client. However, it is also good to communicate the other options we explored, demonstrating a thoroughly researched project.

Here, we review the different models we tried for the case study problem, the hyperparameters we needed to tune, and the results from cross-validation or the validation set in the case of XGBoost. We only include the work we did using all possible features, not the earlier exploratory models where we used only one or two features:

Summary of modeling activities with case study data

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Introduction

Data Exploration and Cleaning

(Challenge) Exploring Remaining Financial Features in Dataset

Introduction to scikit-learn and Model Evaluation

(Challenge) Logistic Regression and Precision-Recall Curve

Details of Logistic Regression and Feature Extraction

(Challenge) Logistic Regression Model and Coefficients

The Bias-Variance Trade-Off

(Challenge) Cross-Validation and Feature Engineering

Decision Trees and Random Forests

(Challenge) Cross-Validation Grid Search with Random Forest

Gradient Boosting, XGBoost, and SHAP Values

(Challenge) XGBoost and SHAP Explanation for Case Study Data

Test Set Analysis, Financial Insights, and Delivery to the Client

(Challenge) Deriving Financial Insights

Appendix

Review of Modeling Results

Summary of modeling activities with case study data