针对不同的分类器的效果比较：

不同分类器效果比较.png

import warnings 
warnings.filterwarnings('ignore')

import numpy as np
np.random.seed(0)

import matplotlib.pyplot as plt

from sklearn.datasets import make_classification
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.calibration import calibration_curve

n_samples = 10000
n_features = 20
n_informative = 2
n_redundant = 2

X,y = make_classification(n_samples=n_samples,n_features=n_features,
                          n_informative=n_informative,n_redundant=n_redundant)

train_samples = 100 

X_train = X[:train_samples]
X_test = X[train_samples:]
y_train = y[:train_samples]
y_test = y[train_samples:]

lr = LogisticRegression()
svc = LinearSVC(C=1.0)
rfc = RandomForestClassifier()
gnb = GaussianNB()

plt.figure(figsize=(10, 10))
ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((3, 1), (2, 0))

ax1.plot([0, 1], [0, 1], "k:", label="Perfectly calibrated")

for clf, name in [(lr, 'Logistic'),
                  (gnb, 'Naive Bayes'),
                  (svc, 'Support Vector Classification'),
                  (rfc, 'Random Forest')]:
    clf.fit(X_train, y_train)
    if hasattr(clf, "predict_proba"):
        prob_pos = clf.predict_proba(X_test)[:, 1]
    else:  # use decision function
        prob_pos = clf.decision_function(X_test)
        prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())
    fraction_of_positives, mean_predicted_value = calibration_curve(y_test, prob_pos, n_bins=10)

    ax1.plot(mean_predicted_value, fraction_of_positives, "s-",
             label="%s" % (name, ))

    ax2.hist(prob_pos, range=(0, 1), bins=10, label=name,
             histtype="step", lw=2)

ax1.set_ylabel("Fraction of positives")
ax1.set_ylim([-0.05, 1.05])
ax1.legend(loc="lower right")
ax1.set_title('Calibration plots  (reliability curve)')

ax2.set_xlabel("Mean predicted value")
ax2.set_ylabel("Count")
ax2.legend(loc="upper center", ncol=2)

plt.tight_layout()
plt.show()