Programming scikit-learn

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1. Check the scikit-learn Help Pages.


2. Apply for a UND Engineering Linux Account.


3. Log in to the Linux Server undcemcs02.und.edu .


4. Download and Install the scikit-learn.
Enter the following Linux command:


shell> pip3 install -U scikit-learn 


5. Download and Install the matplotlib.
Enter the following Linux command:


shell> pip3 install matplotlib 

or other packages like


shell> pip3 install pandas 


6. Start Developing the Applications; for example,



A kNN Example Using scikit-learn
All data points	A new data point classified
Refresh after execution
# Three lines to make Python compiler able to draw: import sys import matplotlib matplotlib.use( 'Agg' ) import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier # a training set x = [ 4, 5, 10, 4, 3, 11, 14, 8, 10, 12 ] y = [ 21, 19, 24, 17, 16, 25, 24, 22, 21, 21 ] classes = [ 0, 0, 1, 0, 0, 1, 1, 0, 1, 1 ] data = list( zip( x, y ) ) # k = 5 knn = KNeighborsClassifier( n_neighbors=5 ) # Fit a kNN model on the model using 5 nearest neighbors. knn.fit( data, classes ) # a new point new_x = 8 new_y = 21 new_point = [( new_x, new_y )] # Predict the class of new, unforeseen data points. prediction = knn.predict( new_point ) # Plot all points by un-commenting the next command. plt.scatter( x, y, c=classes ) # Classify a new point by un-commenting the next 2 commands. # plt.scatter( x + [new_x], y + [new_y], c=classes + [prediction[0]] ) # plt.text( x=new_x-1.7, y=new_y-0.7, s=f"new point, class: {prediction[0]}" ) plt.show( ) # Two lines to make Python compiler able to draw: plt.savefig( sys.stdout.buffer ) sys.stdout.flush( )