Gradient Boosting

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Work with your assigned partner to write a Python script to fit a variety of models to this used car quality data. The goal is to predict quality.

Make sure to do the following:

• Feature engineering
• Cross validation: training, dev/validation, & testing split
• Model selection e.g. hidden layer size, number of estimators based on dev/validation performance
• Use the error metrics, per class: Precision, Recall & F1
• Make a confusion matrix

Try the following models:

• Logistic Regression
• MLP
• Decision Tree
• Random Forest
• Adaboost
• GradientBoosting

Regression with Neural Networks

Revisit the rental data, this time fit multiple regression models to predict the price. Use MAE, & MAPE to measure the accuracy of the following models:

• Linear Regression
• MLP Regressor
• Decision Tree Regressor
• Random Forest Regressor
• Adaboost Regressor
• Gradient Boost Regressor

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1. GFU
2. CSIS
3. J. Walker Orr
4. CSIS 441