8
単純なRandomForestRegressorの例を実行しようとしています。しかし、精度をテストしている間、私はこのエラーを受け取ります。RandomForestRegressorでサポートされていないエラーが発生しました
/Users/noppanit/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc in accuracy_score(y_true, y_pred, normalize, sample_weight)
177
178 # Compute accuracy for each possible representation
--> 179 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
180 if 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 not in ["binary", "multiclass", "multilabel-indicator",
91 "multilabel-sequences"]):
---> 92 raise ValueError("{0} is not supported".format(y_type))
93
94 if y_type in ["binary", "multiclass"]:
ValueError: continuous is not supported
これはデータのサンプルです。私は実際のデータを表示することはできません。
target, func_1, func_2, func_2, ... func_200
float, float, float, float, ... float
ここに私のコードです。
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.
誰もが回帰の分類などの予測やテスト値を比較する方法を知っていますか？ – Priyansh