本文整理汇总了Python中sklearn.ensemble.RandomForestClassifier.partial_fit方法的典型用法代码示例。如果您正苦于以下问题：Python RandomForestClassifier.partial_fit方法的具体用法？Python RandomForestClassifier.partial_fit怎么用？Python RandomForestClassifier.partial_fit使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.ensemble.RandomForestClassifier的用法示例。
在下文中一共展示了RandomForestClassifier.partial_fit方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: xrange
# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import partial_fit [as 别名]
tp += 1
if y_t != y_p and y_t == 1:
fn += 1
if y_t != y_p and y_t == 0:
fp += 1
if y_t == y_p and y_t == 0:
tn += 1
return tp, tn, fp, fn
if classification:
#print "PCA ratios: ", pca.explained_variance_ratio_
if partialFit:
for i in xrange(10):
clf.partial_fit(X_tr, Y_tr, [0,1])
tp, tn, fp, fn = _tp_tn_fp_fn(Y_st, clf.predict(X_tst))
print tp, tn, fp, fn
print "tp", "tn", "fp", "fn"
print "Accuracy ", (tp+tn)/(tp+tn+fp+fn), "Negative precision ", tn/(tn+fn+0.0001), "Precision ", tp/(tp+fp+0.00001)
print "True performance ", tn/(fp+tn)
else:
clf.fit(X_tr, Y_tr)
tp, tn, fp, fn = _tp_tn_fp_fn(Y_st, clf.predict(X_tst))
print tp, tn, fp, fn
print "tp", "tn", "fp", "fn"
print "Accuracy ", (tp+tn)/(tp+tn+fp+fn), "Negative precision ", tn/(tn+fn+0.0001), "Precision ", tp/(tp+fp+0.00001)
print "True performance ", tn/(fp+tn)
import cPickle