python sklearn ã§ãƒã‚¸ã‚¹ãƒ†ã‚£ãƒƒã‚¯å›žå¸°ã€‚fit ã—㦠predictã€å¼•æ•° C ã§æ£å‰‡åŒ– | コード７区簡å˜ãªãƒ‡ãƒ¼ã‚¿ã§ãƒã‚¸ã‚¹ãƒ†ã‚£ã‚¯å›žå¸°ã‚’試ã—ã¦ã¿ã¾ã™ã€‚ã‚„ã‚‹ã®ã¯ï¼’クラスã®åˆ†é¡žã§ã™ãŒã€ç†è«–çš„ãªã“ã¨ã¯ã¨ã‚Šã‚ãˆãšç½®ã„ã¨ã„ã¦ã€ python ã® scikit-learnライブラリ を使ã£ã¦ã¿ã¾ã™ã€‚ã“ã®ãƒ©ã‚¤ãƒ–ラリ㮠LogisticRegression ã¨ã„ã†ã‚¯ãƒ©ã‚¹ãŒãƒã‚¸ã‚¹ãƒ†ã‚£ãƒƒã‚¯å›žå¸°ã§ã™ã€‚ メソッド fitã€predictã€scoreã€å±žæ€§ coef_ã€intercept_ã€ãƒ‘ラメータ C を使ã£ã¦ã¿ã¾ã—ãŸã€‚ from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt import numpy as np np.random.seed(seed=0) X_0 = np.random.multiv
Data Science
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