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我只是想做一个简单的RandomForestRegressor示例.但在测试准确性时,我得到了这个错误

/Users/noppanit/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc

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in accuracy_score(y_true,y_pred,normalize,sample_weight)177 178#计算每种可能表示的准确性 - > 179 y_type,y_true,y_pred = _check_targets(y_true,y_pred)180如果y_type.startswith('multilabel'):181 differing_labels = count_nonzero(y_true - y_pred,axis = 1)

/Users/noppanit/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc

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in _check_targets(y_true,y_pred)90 if(y_type不在["binary","multiclass","multilabel-indicator",91"multilabel-sequences"]):---> 92引发ValueError("{0}是不支持".format(y_type)"93 94如果["binary","multiclass"]中的y_type:

ValueError: continuous is not supported

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这是数据的样本.我无法显示真实数据.

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 = …