Python Dataframe：每次运行随机森林回归器时均方根误差分数不同

我目前使用以下代码运行随机森林模型。我将 random_state 设置为 100。

from sklearn.cross_validation import train_test_split

X_train_RIA_INST_PWM, X_test_RIA_INST_PWM, y_train_RIA_INST_PWM, y_test_RIA_INST_PWM = train_test_split(X_RIA_INST_PWM, Y_RIA_INST_PWM, test_size=0.3, random_state = 100)



# Random Forest Regressor for RIA_INST_PWM accounts  

import numpy as np
from sklearn.ensemble import RandomForestRegressor

regressor_RIA_INST_PWM = RandomForestRegressor(n_estimators=100, min_samples_split = 10)
regressor_RIA_INST_PWM.fit(X_RIA_INST_PWM, Y_RIA_INST_PWM)

print ("R^2 for training set:"),
print (regressor_RIA_INST_PWM.score(X_train_RIA_INST_PWM, y_train_RIA_INST_PWM))

print ('-'*50)

print ("R^2 for test set:"),
print (regressor_RIA_INST_PWM.score(X_test_RIA_INST_PWM, y_test_RIA_INST_PWM))

然后我使用以下代码来计算预测值。

def predict_AUM(df, features, regressor):

    # Reset index for later merge of predicted target values with Account IDs
    df.reset_index();

    # Set predictor variables 
    X_Predict = df[features]

    # Clean inputs 
    X_Predict = X_Predict.replace([np.inf, -np.inf], np.nan)
    X_Predict = X_Predict.fillna(0)

    # Predict Current_AUM
    Y_AUM_Snapshot_1yr_Predict = regressor.predict(X_Predict)
    df['PREDICTED_SPAN'] = Y_AUM_Snapshot_1yr_Predict

    return df 

df_EVENT5_20 = predict_AUM(df_EVENT5_19, dfzip_features_AUM_RIA_INST_PWM, regressor_RIA_INST_PWM)

最后，我计算结果的 RMSE：

from sklearn.metrics import mean_squared_error
from math import sqrt

rmse = sqrt(mean_squared_error(df_EVENT5_20['SPAN_DAYS'], df_EVENT5_20['PREDICTED_SPAN']))
rmse

每次我运行我的代码......我的 RMSE 都会改变。它从 7.75 变化到 16.4 为什么会发生这种情况？每次运行代码时如何才能获得相同的 RMSE？此外，如何针对 RMSE 优化模型？

您仅播种了 train_test_split，这确保了训练集和测试集的数据随机分配是可重复的。

顾名思义RandomForestRegressor 还包含算法中依赖于随机数的部分（例如，数据的特定不同部分或用于训练单个决策树的不同特征）。如果你想要可重复的结果，你也需要播种。为此，您需要使用 random_state 来初始化它，如下所示：

regressor_RIA_INST_PWM = RandomForestRegressor(
                           n_estimators=100, 
                           min_samples_split = 10, 
                           random_state=100
                         )