Loose Coupling

#!/usr/bin/python """ skeleton code for k-means clustering mini-project """ import pickle import numpy import matplotlib.pyplot as plt import sys sys.path.append("../tools/") from feature_format import featureFormat, targetFeatureSplit def Draw(pred, features, poi, mark_poi=False, name="image.png", f1_name="feature 1", f2_name="feature 2"): """ some plotting code designed to help you visualize your clusters """ ### plot each cluster with a different color--add more colors for ### drawing more than 4 clusters colors = ["b", "c", "k", "m", "g"] for ii, pp in enumerate(pred): plt.scatter(features[ii][0], features[ii][1], color = colors[pred[ii]]) ### if you like, place red stars over points that are POIs (just for funsies) if mark_poi: for ii, pp in enumerate(pred): ...

sklearn decision tree split을 2, 50로 설정하고 accuracy를 비교한다. import  sys from  class_vis  import  prettyPicture from  prep_terrain_data  import  makeTerrainData   import  matplotlib.pyplot as plt import  numpy as np import  pylab as pl   features_train, labels_train, features_test, labels_test  =  makeTerrainData()   ########################## DECISION TREE ################################# ### your code goes here--now create 2 decision tree classifiers, ### one with min_samples_split=2 and one with min_samples_split=50 ### compute the accuracies on the testing data and store ### the accuracy numbers to acc_min_samples_split_2 and ### acc_min_samples_split_50,...