python - 随机森林准确率太低

Question

我想使用 randomforest 来预测电力消耗。数据调整后，最新情况如下

X=df[['Temp(⁰C)','Araç Sayısı (adet)','Montaj V362_WH','Montaj V363_WH','Montaj_Temp','avg_humidity']]

X.head(15)

输出:

Temp(⁰C)    Araç Sayısı (adet)  Montaj V362_WH  Montaj V363_WH  Montaj_Temp avg_humidity
0   3.250000    0.0 0.0 0.0 17.500000   88.250000
1   3.500000    868.0   16.0    18.0    20.466667   82.316667
2   3.958333    774.0   18.0    18.0    21.166667   87.533333
3   6.541667    0.0 0.0 0.0 18.900000   83.916667
4   4.666667    785.0   16.0    18.0    20.416667   72.650000
5   2.458333    813.0   18.0    18.0    21.166667   73.983333
6   -0.458333   804.0   16.0    18.0    20.500000   72.150000
7   -1.041667   850.0   16.0    16.0    19.850000   76.433333
8   -0.375000   763.0   16.0    18.0    20.500000   76.583333
9   4.375000    1149.0  16.0    16.0    21.416667   84.300000
10  8.541667    0.0 0.0 0.0 21.916667   71.650000
11  6.625000    763.0   16.0    18.0    22.833333   73.733333
12  5.333333    783.0   16.0    16.0    22.166667   69.250000
13  4.708333    764.0   16.0    18.0    21.583333   66.800000
14  4.208333    813.0   16.0    16.0    20.750000   68.150000

y.head(15)

输出:

    Montaj_ET_kWh/day
0   11951.0
1   41821.0
2   42534.0
3   14537.0
4   41305.0
5   42295.0
6   44923.0
7   44279.0
8   45752.0
9   44432.0
10  25786.0
11  42203.0
12  40676.0
13  39980.0
14  39404.0

   X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.30, random_state=None)

   clf = RandomForestRegressor(n_estimators=10000, random_state=0, n_jobs=-1)
   clf.fit(X_train, y_train['Montaj_ET_kWh/day'])
   for feature in zip(feature_list, clf.feature_importances_):
        print(feature)

输出

  ('Temp(⁰C)', 0.11598075020423881)
  ('Araç Sayısı (adet)', 0.7047301384616493)
  ('Montaj V362_WH', 0.04065706901940535)
  ('Montaj V363_WH', 0.023077554218712878)
  ('Montaj_Temp', 0.08082006262985514)
  ('avg_humidity', 0.03473442546613837)


 sfm = SelectFromModel(clf, threshold=0.10)
 sfm.fit(X_train, y_train['Montaj_ET_kWh/day'])

 for feature_list_index in sfm.get_support(indices=True):
      print(feature_list[feature_list_index])

输出:

  Temp(⁰C)
  Araç Sayısı (adet)

 X_important_train = sfm.transform(X_train)
 X_important_test = sfm.transform(X_test)

 clf_important = RandomForestRegressor(n_estimators=10000, random_state=0, n_jobs=-1)
 clf_important.fit(X_important_train, y_train)
 y_test=y_test.values
 y_pred = clf.predict(X_test)
 y_test=y_test.reshape(-1,1)
 y_pred=y_pred.reshape(-1,1)
 y_test=y_test.ravel()
 y_pred=y_pred.ravel()
 label_encoder = LabelEncoder()
 y_pred = label_encoder.fit_transform(y_pred)
 y_test = label_encoder.fit_transform(y_test)

 accuracy_score(y_test, y_pred)

输出:

 0.010964912280701754

我不知道为什么准确度太低，不知道我在哪里犯了错误

Answer 1

您的错误是您在回归设置中要求准确性(分类指标)，这是毫无意义的。

来自accuracy_score documentation (强调):

sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)

Accuracy classification score.

检查list of metrics可在 scikit-learn 中获取合适的回归指标(您还可以在其中确认准确性仅用于分类)；更多详情请参阅我的回答Accuracy Score ValueError: Can't Handle mix of binary and continuous target