Sklearn:如何向Sklearn RandomForestClassifier提供数据

Question

我有这些数据:

print training_data
print labels
# prints
[[1, 0, 1, 1], [1, 1, 1, 1], [1, 0, 1, 1], [1, 1, 1, 0], [1, 1, 0, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 0,0], [1, 1, 1, 1], [1, 0, 1, 1]]
['a', 'b', 'a', 'b', 'a', 'b', 'b', 'a', 'a', 'a', 'b']

我试图从sklearn python库中将其提供给RandomForestClassifier。

classifier = RandomForestClassifier(n_estimators=10)
classifier.fit(training_data, labels)

但是收到这个错误:

Traceback (most recent call last):
  File "learn.py", line 52, in <module>
    main()
  File "learn.py", line 48, in main
    classifier = train_classifier()
  File "learn.py", line 33, in train_classifier
    classifier.fit(training_data, labels)
  File "/Library/Python/2.7/site-packages/scikit_learn-0.14_git-py2.7-macosx-10.8-intel.egg/sklearn/ensemble/forest.py", line 348, in fit
    y = np.ascontiguousarray(y, dtype=DOUBLE)
  File "/Library/Python/2.7/site-packages/numpy-1.8.0.dev_bbcfcf6_20130307-py2.7-macosx-10.8-intel.egg/numpy/core/numeric.py", line 419, in ascontiguousarray
    return array(a, dtype, copy=False, order='C', ndmin=1)
ValueError: could not convert string to float: a

我的猜测是我没有正确格式化该数据以进行拟合。但我不明白为什么从文档

这似乎是一个非常基本、简单的问题。有人知道答案吗?

Answer 1

尝试使用LabelEncoder预先转换您的标签。

Answer 2

您可以使用由分类器自动识别的numpy数组，如下所示:

import numpy as np
from sklearn.ensemble import RandomForestClassifier
np_training = np.array(training_data)
np_labels = np.array(labels)
clf = RandomForestClassifier(n_estimators=20, max_depth=5)
clf.fit(np_training, np_labels)

应该可以