from sklearn.linear_model import LogisticRegressionLogisticRegression()
# 변수에 저장해서 사용
classifier = LogisticRegression(random_state=2)
# train_test_split 로 나눠둔 학습용 데이터를 입력해 학습
classifier.fit(X_train, y_train)
학습이 끝나면 인공지능을 테스트 해봐야 한다
classifier.predict(테스트값 변수) : 예측한 값을 반환(0과 1로 나온다)
>>> classifier.predict(X_test) # 테스트용 데이터 X_test
array([1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1,
0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,
0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], dtype=int64)classifier.predict_proba() : 예측 확률을 반환
이를 통해 모델이 예측한 결과에 대한 확신 정도를 파악할 수 있다.
>>> classifier.predict_proba(X_test)
array([[0.05222541, 0.94777459],
[0.11605631, 0.88394369],
[0.63700128, 0.36299872],
[0.83052528, 0.16947472],
[0.59207874, 0.40792126],
[0.42025592, 0.57974408],
[0.90931087, 0.09068913],
[0.81084452, 0.18915548],
[0.35558493, 0.64441507],
[0.44843601, 0.55156399],
[0.39815681, 0.60184319],
[0.92707468, 0.07292532],
[0.11132818, 0.88867182],
[0.88910844, 0.11089156],
[0.85013329, 0.14986671],
[0.12538946, 0.87461054],
[0.17940585, 0.82059415],
[0.50770797, 0.49229203],
[0.85703651, 0.14296349],
[0.53347169, 0.46652831],
[0.43599634, 0.56400366],
[0.97117043, 0.02882957],
[0.78015126, 0.21984874],
[0.53529107, 0.46470893],
[0.9006661 , 0.0993339 ],
[0.95881574, 0.04118426],
[0.58447275, 0.41552725],
[0.52381334, 0.47618666],
[0.41482745, 0.58517255],
[0.57627107, 0.42372893],
[0.87272256, 0.12727744],
[0.14814786, 0.85185214],
[0.51649563, 0.48350437],
[0.47313965, 0.52686035],
[0.42611047, 0.57388953],
[0.5575374 , 0.4424626 ],
[0.03603706, 0.96396294],
[0.86972787, 0.13027213],
[0.16185158, 0.83814842],
[0.6975837 , 0.3024163 ],
[0.16247656, 0.83752344],
[0.66723503, 0.33276497],
[0.53690716, 0.46309284],
[0.11724695, 0.88275305],
[0.76838179, 0.23161821],
[0.93975628, 0.06024372],
[0.02570856, 0.97429144],
[0.53847368, 0.46152632],
[0.86447721, 0.13552279],
[0.48291809, 0.51708191],
[0.79448456, 0.20551544],
[0.28831743, 0.71168257],
[0.20759439, 0.79240561],
[0.21777607, 0.78222393],
[0.34080765, 0.65919235],
[0.28658669, 0.71341331],
[0.15100284, 0.84899716],
[0.21902747, 0.78097253],
[0.50242181, 0.49757819],
[0.66480633, 0.33519367],
[0.95087155, 0.04912845],
[0.10503121, 0.89496879],
[0.1937405 , 0.8062595 ],
[0.38877472, 0.61122528],
[0.14896837, 0.85103163],
[0.40624769, 0.59375231],
[0.83190782, 0.16809218],
[0.57135648, 0.42864352],
[0.71870423, 0.28129577],
[0.10205759, 0.89794241],
[0.34353325, 0.65646675],
[0.64469831, 0.35530169],
[0.52147373, 0.47852627],
[0.16248731, 0.83751269],
[0.12033401, 0.87966599],
[0.39117792, 0.60882208],
[0.49433721, 0.50566279],
[0.71563406, 0.28436594],
[0.61285689, 0.38714311],
[0.96545934, 0.03454066],
[0.30554451, 0.69445549],
[0.7257715 , 0.2742285 ],
[0.56977791, 0.43022209],
[0.52689497, 0.47310503],
[0.07670217, 0.92329783],
[0.10641807, 0.89358193],
[0.79014557, 0.20985443],
[0.85495167, 0.14504833],
[0.76739926, 0.23260074],
[0.93914653, 0.06085347],
[0.062172 , 0.937828 ],
[0.27151662, 0.72848338],
[0.88906408, 0.11093592],
[0.65047503, 0.34952497],
[0.88853887, 0.11146113],
[0.71506858, 0.28493142],
[0.08330045, 0.91669955],
[0.67634886, 0.32365114],
[0.83441148, 0.16558852],
[0.60488491, 0.39511509],
[0.72362065, 0.27637935],
[0.33912939, 0.66087061],
[0.91664381, 0.08335619],
[0.68747058, 0.31252942],
[0.58689496, 0.41310504]])'인공지능 > Machine Learning' 카테고리의 다른 글
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