Start : 28 Jun, 2018

• It is easy to learn algorithm while learning machine learning.
• I want to provide the easiest way to try making Machine Learning Algorithm.
• Need to develop the regression
• Making prototype of classifier is done.
• It deals all feature as categorical, not continuos.
• Entropy calcuation has contained. entropy of each feature is important to predicting.

1. feature scaling (not need scaling when calculation entropy)
2. calculate entropy (entropy is weight.)
3. calculate feature weight (some feature is how many important than others)
4. summary data point by level, stack to the recipe (the model)
5. call predict function with test data
6. get the suited point of every feature as test data's value from recipe (the making of Burger Matrix)
7. product weight vector(in 3.) Burger Matrix(in 6.)
8. find the best probable class.

See the practice file, know the using CheeseBurger Library simply.

/example/titanic-cheeseburger.py # the accuracy is 0.72952 (above 72%)

And compare with 'decision Tree' algorithm.

/example/titanic-decision-tree.py # the accuray is 0.77990 (above 77%)

fredriccliver@gmail.com