RandomForestRegressor中的连续不受支持错误

Question

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

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

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

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 = 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.

Answer 1

这是因为accuracy_score仅用于分类任务.对于回归,您应该使用不同的东西,例如:

clf.score(X_test, y_test)

在X_test是样本的情况下,y_test是对应的地面实况值.它将计算内部的预测.

Answer 2

由于您正在执行回归任务，因此您应该使用度量R 平方 （确定系数）而不是 准确度分数（准确度分数用于分类问题）。

R平方可以通过调用RandomForestRegressor提供的score函数来计算，例如：

rfr.score(X_test,Y_test)