代码语言：javascript
复制log_regress = linear_model.LogisticRegression()
# Train the model
log_regress.fit(X = train_features ,
y = train_label)
# Check trained model intercept
print(log_regress.intercept_)
# Check trained model coefficients
print(log_regress.coef_)
[3.8742755]
[[-0.85532933 -2.30146606 -0.03444764 -0.29622237 -0.00644779 0.00482113
-0.01987031]]
代码语言：javascript
复制 # In[*]
# Make predictions
preds = log_regress.predict(X=test_features)
print(preds)
# In[*]
# Predict the probablities
pred_probs = log_regress.predict_proba(X=test_features)
print(pred_probs)
代码语言：javascript
复制print(pd.crosstab(preds, test_label))
Survived 0 1
row_0
0 92 24
1 14 48
代码语言：javascript
复制 # In[*]
# get the accuracy of the prediction
log_regress.score(X = test_features ,
y = test_label)
0.7865168539325843
- 除了使用crosstab（）函数生成混淆矩阵之外，您还可以使用Scikit中的metrics模块中的confusion_matrix（）函数来学习：
代码语言：javascript
复制 # In[*]
from sklearn import metrics
# view the confusion matrix
metrics.confusion_matrix(
y_true = test_label, # True labels
y_pred = preds) # Predicted labels
代码语言：javascript
复制print(metrics.classification_report(
y_true = test_label,
y_pred = preds))
precision recall f1-score support
0 0.79 0.87 0.83 106
1 0.77 0.67 0.72 72
avg / total 0.79 0.79 0.78 178
代码语言：javascript
复制 # In[*]
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
# convert the probabilities from ndarray to
# dataframe
df_prob = pd.DataFrame(
pred_probs,
columns=['Death', 'Survived'])
fpr, tpr, thresholds = roc_curve(
test_label, df_prob['Survived'])
# find the area under the curve (auc) for the
# ROC
roc_auc = auc(fpr, tpr)
plt.title(
'Receiver Operating Characteristic Curve')
plt.plot(fpr, tpr, 'black',
label='AUC = %0.2f'% roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.1])
plt.ylim([-0.1,1.1])
plt.ylabel('True Positive Rate (TPR)')
plt.xlabel('False Positive Rate (FPR)')
plt.show()
