title: Random forest accuracy too low
body: <p>I want to predict an electric consumption by using randomforest . after making regulation on data , latest status is as below </p>
  
  <pre><code>X=df[['Temp(⁰C)','Araç Sayısı (adet)','Montaj V362_WH','Montaj V363_WH','Montaj_Temp','avg_humidity']]
  
  X.head(15)
  </code></pre>
  
  <p>Output:</p>
  
  <pre><code>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)
  </code></pre>
  
  <p>Output:</p>
  
  <pre><code>    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)
  </code></pre>
  
  <p>OUTPUT</p>
  
  <pre><code>  ('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])
  </code></pre>
  
  <p>OUTPUT:</p>
  
  <pre><code>  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)
  </code></pre>
  
  <p>output :</p>
  
  <pre><code> 0.010964912280701754
  </code></pre>
  
  <p>I have no idea why accuracy was too low , any idea where I made mistake </p>