Principal Components Analysis - Georgia Tech

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Udacity - Retrieve Accuracy using Decision Tree

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,...

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[Android] Android Studio Github 설정하기

1. Settings File > Settings 2. Version Control 메뉴 or 검색창에서 git 입력 3. 사용중인 Github 계정 및 비번을 입력하고 'Apply' > 'OK'색에서 'git' 검색 4. VCS > Checkout from Version Control > Github 5. Clone Repository에 Checkout 받을 프로젝트를 선택한 후, 'Clone'을 실행

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[machine learning] k means algorithm

#!/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): ...

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Loose Coupling

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