sklearn随机森林准确性分数相同的训练和测试数据

Question

我试图建立一个电动汽车充电事件数据的分类模型。我想预测充电站是否可以在特定的时间点使用。我有下面的代码工作：

from sklearn.ensemble import RandomForestClassifier 
import pandas as pd 

raw_data = pd.read_csv('C:/temp/sample_dataset.csv') 
raw_test = pd.read_csv('C:/temp/sample_dataset_test.csv') 
print ('raw data shape: ', raw_test.shape) 

#choose which columns to dummify 
X_vars = ['station_id', 'day_of_week', 'epoch', 'station_city', 
'station_county', 'station_zip', 'port_level', 'perc_local_occupancy', 
'ports_at_station', 'avg_charge_duration'] 
y_var = ['target_variable'] 
categorical_vars = ['station_id','station_city','station_county'] 

#split X and y in training and test 
X_train = raw_data.loc[:,X_vars] 
y_train = raw_data.loc[:,y_var] 
X_test = raw_test.loc[:,X_vars] 
y_test = raw_test.loc[:,y_var] 

#make dummy variables 
X_train = pd.get_dummies(X_train, columns = categorical_vars) 
X_test = pd.get_dummies(X_test, columns=categorical_vars) 

print('train size', X_train.shape, '\ntest size', X_test.shape) 

# Train uncalibrated random forest classifier on whole train and evaluate on test data 
clf = RandomForestClassifier(n_estimators=100, max_depth=2) 
clf.fit(X_train, y_train.values.ravel()) 

print ('RF accuracy: TRAINING', clf.score(X_train,y_train)) 
print ('RF accuracy: TESTING', clf.score(X_test,y_test)) 

结果

raw data shape: (1000000, 15) 
train size (1000000, 125) 
test size (1000000, 125) 
RF accuracy: TRAINING 0.831456 
RF accuracy: TESTING 0.831456 

我的问题是，为什么是训练和测试精度完全一样？我跑了很多次，总是一模一样。有任何想法吗？ （我已检查确保原始数据不同）

Answer 1

那么根本就在你的代码一个错字，因为每次你选择的所有行：

#split X and y in training and test 
X_train = raw_data.loc[:,X_vars] 
y_train = raw_data.loc[:,y_var] 
X_test = raw_test.loc[:,X_vars] 
y_test = raw_test.loc[:,y_var] 

您应该对其进行索引分别由一些指标，例如：X_train = raw_data.loc[:idx,X_vars]

Answer 2

您是否有可能在列车和测试文件中使用同一组数据？

如果数据相同，那么最好将数据拆分为train并使用train_test_split模块进行测试。

http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html