machine learning/로지스틱 회귀, 소프트맥스 회귀

로지스틱 회귀

import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets

iris = datasets.load_iris()

iris.keys()

dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])

X = iris["data"][:,3:] # 꽃잎 넓이

y = (iris["target"] == 2).astype(np.int32) # Virginica 이면 1, 아니면 0

from sklearn.linear_model import LogisticRegression

log_reg = LogisticRegression(random_state = 42)
log_reg.fit(X, y)

LogisticRegression(random_state=42)

X_new = np.linspace(0, 3, 1000).reshape(-1, 1)
y_proba = log_reg.predict_proba(X_new)

decision_boundary = X_new[y_proba[:, 1] >= 0.5][0] #확률이 50%가 넘기 시작한 제일 처음 샘플
plt.figure(figsize=(8, 3))
plt.plot(X[y==0], y[y==0], "bs")
plt.plot(X[y==1], y[y==1], "g^")
plt.plot([decision_boundary, decision_boundary], [-1, 2], "k:", linewidth=2)
plt.plot(X_new, y_proba[:, 1], "g-", linewidth=2, label="Iris-Virginica")
plt.plot(X_new, y_proba[:, 0], "b--", linewidth=2, label="Not Iris-Virginica")
plt.legend(loc="center left", fontsize=14)
plt.axis([0, 3, -0.02, 1.02])
plt.show()

decision_boundary

array([1.66066066])

log_reg.predict([[1.7],[1.5]])

array([1, 0])

소프트맥스

X = iris["data"][:,(2,3)] # 꽃잎 길이, 꽃잎 넓이

y = iris["target"]

softmax_reg = LogisticRegression(multi_class="multinomial", solver="lbfgs", C=10 ,random_state=42)

softmax_reg.fit(X, y)

LogisticRegression(C=10, multi_class='multinomial', random_state=42)

softmax_reg.predict([[5,2]])

array([2])

softmax_reg.predict_proba([[5,2]])

array([[6.38014896e-07, 5.74929995e-02, 9.42506362e-01]])