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1. Concepts & Definitions
1.1. Regression versus Classification
1.3. Parameter versus Hyperparameter
1.4. Training, Validation, and Test
2. Problem & Solution
2.1. Gaussian Mixture x K-means on HS6 Weight
2.2. Evaluation of classification method using ROC curve
2.3. Comparing logistic regression, neural network, and ensemble
2.4. Fruits or not, split or encode and scale first?
Challenge: Comparing different models for computing credit risk!!
Challenge: Comparing different models for computing credit risk!!
The previous section, "2.2. Evaluation of Classification Methods using ROC curve" on Track 10, showed how to apply a Logistic Regression in trying to predict credit risk measures. The purpose of this section is to adapt the content of the previous section in a manner to enables the application of a Neural Network and an Ensemble made of Logistic Regression and Neural Network.
The final result should present the ROC curve of the three methods like the graphic presented in the next Figure.
The Python code with all the steps is summarized in this Google Colab (click on the link):
https://colab.research.google.com/drive/1DlXYsNTidZsRf30bUl1GlJ4r9mBwzTP4?usp=sharing
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