import numpy as np
from sklearn.datasets import load_iris
from threshold import learn_model, apply_model, accuracy
data = load_iris()
features = data['data']
labels = data['target_names'][data['target']]
setosa = (labels == 'setosa')
features = features[~setosa]
labels = labels[~setosa]
virginica = (labels == 'virginica')
testing = np.tile([True, False], 50)
training = ~testing
model = learn_model(features[training], virginica[training])
train_error = accuracy(features[training], virginica[training], model)
test_error = accuracy(features[testing], virginica[testing], model)
print('''\
Training error was {0:.1%}.
Testing error was {1:.1%} (N = {2}).
'''.format(train_error, test_error, testing.sum()))
from load import load_dataset
import numpy as np
from threshold import learn_model, apply_model, accuracy
features,labels = load_dataset('seeds')
labels = labels == 'Canadian'
error = 0.0
for fold in range(10):
training = np.ones(len(features), bool)
training[fold::10] = 0
testing = ~training
model = learn_model(features[training], labels[training])
test_error = accuracy(features[testing], labels[testing], model)
error += test_error
error /= 10.0
print('Ten fold cross-validated error was {0:.1%}.'.format(error))
# This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
from load import load_dataset
import numpy as np
from threshold import learn_model, apply_model, accuracy
features, labels = load_dataset('seeds')
labels = labels == 'Canadian'
error = 0.0
for fold in range(10):
training = np.ones(len(features), bool)
training[fold::10] = 0
testing = ~training
model = learn_model(features[training], labels[training])
test_error = accuracy(features[testing], labels[testing], model)
error += test_error
error /= 10.0
print('Ten fold cross-validated error was {0:.1%}.'.format(error))
# published by PACKT Publishing
#
# It is made available under the MIT License
from matplotlib import pyplot as plt
import numpy as np
from sklearn.datasets import load_iris
from threshold import learn_model, apply_model, accuracy
data = load_iris()
features = data['data']
labels = data['target_names'][data['target']]
setosa = (labels == 'setosa')
features = features[~setosa]
labels = labels[~setosa]
virginica = (labels == 'virginica')
testing = np.tile([True, False], 50)
training = ~testing
model = learn_model(features[training], virginica[training])
train_error = accuracy(features[training], virginica[training], model)
test_error = accuracy(features[testing], virginica[testing], model)
print('''\
Training error was {0:.1%}.
Testing error was {1:.1%} (N = {2}).
'''.format(train_error, test_error, testing.sum()))
