def callSuitable(mlalgo, v2):
"""use and evaluate the selected Machine Learning algorithm"""
global X, Y, XT, YT
if mlalgo == 'Decision Tree':
model = models.DTModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'Naive Bayes':
model = models.NBModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'SVM':
model = models.SVMModel(X, Y, XT, YT, v2)
model.start()
else:
model = models.KNNModel(X, Y, XT, YT, v2)
model.start()
def callSuitable(mlalgo, v2):
#evaluate the selected Machine Learning algorithm
global X, Y, XT, YT
if mlalgo == 'Decision Tree':
model = models.DTModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'Naive Bayes':
model = models.NBModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'SVM':
model = models.SVMModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'K Nearest Neighbours':
model = models.KNNModel(X, Y, XT, YT, v2)
model.start()
elif mlalgo == 'Logistic Regression':
model = models.LogModel(X, Y, XT, YT, v2)
model.start()
else:
model = models.ANNModel(X, Y, XT, YT, v2)
model.start()
def loadModel(modelName, fileName=None):
"""load the modelName ML model and test the accuracy"""
global X, Y, XT, YT
mlalgo = modelName
if mlalgo == 'Decision Tree':
model = models.DTModel(X, Y, XT, YT)
model.start()
elif mlalgo == 'Naive Bayes':
model = models.NBModel(X, Y, XT, YT)
model.start()
elif mlalgo == 'SVM':
model = models.SVMModel(X, Y, XT, YT)
model.start()
elif mlalgo == 'K Nearest Neighbours':
model = models.KNNModel(X, Y, XT, YT)
model.start()
elif mlalgo == 'Logistic Regression':
model = models.LogModel(X, Y, XT, YT)
model.start()
else:
model = models.ANNModel(X, Y, XT, YT)
model.start()
