我正在嘗試使用一個簡單的邏輯回歸示例 sklearn.linear_model.LogisticRegression這是代碼：import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.linear_model import LogisticRegressionfrom sklearn import metrics# some randomly generated data with two well differentiated groups x1 = np.random.normal(loc=15, scale=2, size=(30,1))y1 = np.random.normal(loc=10, scale=2, size=(30,1))x2 = np.random.normal(loc=25, scale=2, size=(30,1))y2 = np.random.normal(loc=20, scale=2, size=(30,1))data1 = np.concatenate([x1, y1, np.zeros(shape=(30,1))], axis=1)data2 = np.concatenate([x2, y2, np.ones(shape=(30,1))], axis=1)dfa = pd.DataFrame(data=data1, columns=["F1", "F2", "group"])dfb = pd.DataFrame(data=data2, columns=["F1", "F2", "group"])df = pd.concat([dfa, dfb], ignore_index=True)# the actual fittingfeatures = [item for item in df.columns if item not in ("group")]logreg = LogisticRegression(verbose=1)logreg.fit(df[features], df.group)# plotting and checking the resulttheta = logreg.coef_[0,:] # parametersy0 = logreg.intercept_    # interceptprint("Theta =", theta)print("Intercept = ", y0)xdb = np.arange(0, 30, 0.2)  # dummy x vector for decision boundaryydb = -(y0+theta[0]*xdb) / theta[1] # decision boundary y valuesfig = plt.figure()ax = fig.add_subplot(111)colors = {0 : "red", 1 : "blue"}for i, group in df.groupby("group"):    plt.plot(group["F1"], group["F2"],             MarkerFaceColor = colors[i], Marker = "o", LineStyle="",             MarkerEdgeColor=colors[i])plt.plot(xdb, ydb, LineStyle="--", Color="b")令人震驚的是，結(jié)果圖如下所示：事實(shí)上，準(zhǔn)確度可以計算為：predictions = logreg.predict(df[features])metrics.accuracy_score(predictions, df["group"])結(jié)果是 0.966...我一定是做錯了什么，只是想不通是什么。任何幫助深表感謝！