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python辅导 | Data Science and Machine Learning

python辅导机器学习和数据分析
Assignment 1: Perceptron for Binary Classification  
Loyola Marymount University  
Professor Alex Wong  
CSMI-533: Data Science and Machine Learning  
Given the UCI Breast Cancer dataset, classify whether a tumor is benign or malignant  
Results using the scikit-learn implementation of a Perceptron model is provided.  
You will implement the PerceptronBinaryclass using the Perceptron Learning Algorithm.  
A skeleton of the class is provided in assignment1.py  
Here is the sample output:  
Results using scikit-learn Perceptron modelꢀ  
Training set mean accuracy: 0.9199ꢀ  
Testing set mean accuracy: 0.9298ꢀ  
Results using our Perceptron modelꢀ  
Training set mean accuracy: 0.0000ꢀ  
Testing set mean accuracy: 0.0000ꢀ  
You will complete the following functions for the assignment:  
1
) __update :Update the weight vector during each training iteration  
) fit :Fits the model to x and y by calling the calls __updatefunction, which updates  
the weight vector based on mis-classified examples for t iterations until convergence  
) predict :Predicts the label for each feature vector x  
2
3
4
) score :Predicts labels based on feature vector x by calling predictfunction and  
computes the mean accuracy of the predictions  
Submission:  
You will submit the following to Bright Space  
1) <last_name>_<first_name>_assignment1.py  
Grading:  
I will be executing the assignment using the following command:  
python <last_name>_<first_name>_assignment1.pyꢀ  
Your code must run for me to assign points!  
Your assignment will be graded on:  
1) the correctness of your implementation of the PerceptronBinary  
2) results of your PerceptronBinarymodel on the UCI Breast Cancer dataset  
Late Policy:  
For each day the assignment is late, 50% of its worth will be deducted, e.g. 100% on time, 50%  
1
day late, 25% 2 days late, etc.  

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