AI Demo (model training)

model_training.py

# creating a function automatically (model training)

# 1. assemble some data with known answers (aka 'class' or 'label')
data = [[pizza,good],[spinach,bad],[cake,good]]

# 2. choose a portion of records for testing accuracy later
train = [[pizza,good],[spinach,bad]]
test = [[cake,good]]                  # if input cake, expect 'good'

# 3. extract the features for training set you think matter
# [type, smell, spice, temp, sweetness, color, class]
cleandata = [
  [4, 20, 1, 135, 55, 2, 1], // pizza
  [2, 12, 1, 89, 30, 4, 0], // spinach
]

# 4. separate out the known answers from the inputs (aka features)
train_x = [[4,20,1,135,55,2],[2,12,1,89,30,4]]    # features
train_y = [[1],[0]]                               # class or labels

# 5. choose which type of function to create (trial/error)
myclassifier = SomeAlgorithmFromSomeFramework(line_type) # ignore

# 6. train your 'model' (function)
myclassifier.fit(train_x, train_y) # see goal seek example below

# 7. test accuracy
# a. extract features from test data you set aside
# b. pass in test data features and get prediction
# c. compare prediction to known value to see if it's right