Python

Machine Learning Classifier

Machine Learning Classifiers can be used to predict.

Start with training data. Training data is fed to the classification algorithm. After training the classification algorithm (the fitting function), you can make predictions.

Related course: Data Science and Machine Learning with Python – Hands On!

Example

In the example below we predict if it’s a male or female given vector data.

We start with training data. In this example we have a set of vectors (height, weight, shoe size) and the class this vector belongs to:

#{height, weights, shoe size}
X = [[190,70,44],[166,65,45],[190,90,47],[175,64,39],[171,75,40],[177,80,42],[160,60,38],[144,54,37]]
Y = ['male','male','male','male','female','female','female','female']

Define a vector for your prediction in the same format (height, weight, size). If you want, you can also get this from console input:

P = [[190,80,46]]

Then we fit the training data and predict in this style:

c = Classifier()
c = c.fit(X,Y)
print "\nPrediction : " + str(c.predict(P))

That gives us this code:

from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import RandomForestClassifier

#{height, weights, shoe size}
X = [[190,70,44],[166,65,45],[190,90,47],[175,64,39],[171,75,40],[177,80,42],[160,60,38],[144,54,37]]
Y = ['male','male','male','male','female','female','female','female']

#Predict for this vector (height, wieghts, shoe size)
P = [[190,80,46]]

#{Decision Tree Model}
clf = DecisionTreeClassifier()
clf = clf.fit(X,Y)
print "\n1) Using Decision Tree Prediction is " + str(clf.predict(P))

#{K Neighbors Classifier}
knn = KNeighborsClassifier()
knn.fit(X,Y)
print "2) Using K Neighbors Classifier Prediction is " + str(knn.predict(P))

#{using MLPClassifier}
mlpc = MLPClassifier()
mlpc.fit(X,Y)
print "3) Using MLPC Classifier Prediction is " + str(mlpc.predict(P))

#{using MLPClassifier}
rfor = RandomForestClassifier()
rfor.fit(X,Y)
print "4) Using RandomForestClassifier Prediction is " + str(rfor.predict(P)) +"\n"

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