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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 to classify a product as fruit or not
Challenge: Comparing different models to classify a product as fruit or not
The previous section, "2.4. Fruits or note, split or encode and scale first?" on Track 10, showed how to apply a Logistic Regression in trying to predict if a product is a fruit or not. 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.
Encode and Scale first
It was also studied for the two new methods (Neural Network and Ensemble) if it does not matter the order of applying data operations in the final results, i.e., employing train-test split first or encode and scale first.
Train-Test Split First
The Python code with all the steps is summarized in this Google Colab (click on the link):
https://colab.research.google.com/drive/1TrpfMXasPR449H73FOhbbFiWLDLC-ag-?usp=sharing
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