我只是想做一个简单的 RandomForestRegressor 示例。但是在测试准确性时我收到此错误
/Users/noppanit/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc
在accuracy_score（y_true，y_pred，标准化，样本权重）中
177
攀上漂亮女局长之后178
--> 179 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
180 if y_type.startswith('multilabel'):
[第 181 章]
/Users/noppanit/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc
在 _check_targets(y_true, y_pred) 中
90 if (y_type 不在 ["binary", "multiclass", "multilabel-indicator",
91“多标签序列”]）：
---> 92 raise ValueError("不支持{0}".format(y_type))
93
94 if y_type in ["binary", "multiclass"]：
ValueError: continuous is not supported
这是数据样本。我无法展示真实数据。
target, func_1, func_2, func_2, ... func_200
float, float, float, float, ... float
这是我的代码。
import pandas as pd
import numpy as np
from sklearn.preprocessing import Imputer
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
from sklearn import tree
train = pd.read_csv('data.txt', sep='\t')
labels = train.target
train.drop('target', axis=1, inplace=True)
cat = ['cat']
train_cat = pd.get_dummies(train[cat])
train.drop(train[cat], axis=1, inplace=True)
train = np.hstack((train, train_cat))
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp.fit(train)
train = imp.transform(train)
x_train, x_test, y_train, y_test = train_test_split(train, labels.values, test_size = 0.2)
clf = RandomForestRegressor(n_estimators=10)
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
accuracy_score(y_test, y_pred) # This is where I get the error.
