로지스틱 회기 (logistic regression)
실습 코드: 날씨 예측의 예 import numpy as np from sklearn.linear_model import LogisticRegression X_train = np.r_[np.random.normal(3,1,size=50), np.random.normal(-1,8,size=50)].reshape((100,-1)) y_train = np.r_[np.ones(50), np.zeros(50)] model = LogisticRegression() model.fit(X_train, y_train) print(model.predict_proba([[0],[1],[2]])[:,1])
Logistic Regression 실습
날씨 예측 문제 import numpy as np from sklearn.linear_model import LogisticRegression import matplotlib.pyplot as plt X_train = np.r_[np.random.normal(3,1,size=50), np.random.normal(-1,8,size=50)].reshape(100,1) y_train = np.r_[np.ones(50), np.zeros(50)] model = LogisticRegression() model.fit(X_train,y_train) print(model.predict_proba([[-1],[0],[2]])[:,0]) print(model.coef_) print(model.intercept_) pr..
