def example1():
target_url = ("https://archive.ics.uci.edu/ml/machine-learning-"
"databases/undocumented/connectionist-bench/sonar/sonar.all-data")
# Instantiate class
ml = MLObject(target_url)
# output summary statistics
ml.data_summary()
def example1():
target_url = (
"https://archive.ics.uci.edu/ml/machine-learning-"
"databases/undocumented/connectionist-bench/sonar/sonar.all-data")
# Instantiate class
ml = MLObject(target_url)
# output summary statistics
ml.data_summary()
def example2():
target_url = ("https://archive.ics.uci.edu/ml/machine-learning-"
"databases/undocumented/connectionist-bench/sonar/sonar.all-data")
# Instantiate class
ml = MLObject(target_url)
# split the data into traning and test datasets
train, test = ml.train_test_split(percentage=0.4)
print train
print test
def example2():
target_url = (
"https://archive.ics.uci.edu/ml/machine-learning-"
"databases/undocumented/connectionist-bench/sonar/sonar.all-data")
# Instantiate class
ml = MLObject(target_url)
# split the data into traning and test datasets
train, test = ml.train_test_split(percentage=0.4)
print train
print test
__author__ = 'shazada nawaz'
from monstera.core import MLObject
target = [1.5, 2.1, 3.3, -4.7, -2.3, 0.75]
prediction = [0.5, 1.5, 2.1, -2.2, 0.1, -0.5]
target_url = ("https://archive.ics.uci.edu/ml/machine-learning-databases"
"/undocumented/connectionist-bench/sonar/sonar.all-data")
ml = MLObject(target_url)
ml.target = [1.5, 2.1, 3.3, -4.7, -2.3, 0.75]
ml.prediction = [0.5, 1.5, 2.1, -2.2, 0.1, -0.5]
ml.model_performance()
__author__ = 'shazada nawaz'
from monstera.core import MLObject
target = [1.5, 2.1, 3.3, -4.7, -2.3, 0.75]
prediction = [0.5, 1.5, 2.1, -2.2, 0.1, -0.5]
target_url = ("https://archive.ics.uci.edu/ml/machine-learning-databases"
"/undocumented/connectionist-bench/sonar/sonar.all-data")
ml = MLObject(target_url)
ml.target = [1.5, 2.1, 3.3, -4.7, -2.3, 0.75]
ml.prediction = [0.5, 1.5, 2.1, -2.2, 0.1, -0.5]
ml.model_performance()