cross_val_score 与 .score 的回归评分结果显着不同

Question

I'm running RandomForestRegressor(). I'm using R-squared for scoring. Why do I get dramatically different results with .score versus cross_val_score? Here is the relevant code:

X = df.drop(['y_var'], axis=1)
y = df['y_var']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

# Random Forest Regression
rfr = RandomForestRegressor()
model_rfr = rfr.fit(X_train,y_train)
pred_rfr = rfr.predict(X_test)
result_rfr = model_rfr.score(X_test, y_test)

# cross-validation
rfr_cv_r2 = cross_val_score(rfr, X, y, cv=5, scoring='r2')

I understand that cross-validation is scoring multiple times versus one for .score, but the results are so radically different, that something is clearly wrong. Here are the results:

R2-dot-score: .99072
R2-cross-val: [0.5349302  0.65832268 0.52918704 0.74957719 0.45649582]

What am I doing wrong? Or what might explain this discrepancy?

EDIT:

OK, I may have solved this. It seems as if cross_val_score does not shuffle the data, which may be leading to worse predictions when data is grouped together. The easiest solution I found (via this answer ) to this was to simply shuffle the dataframe before running the model:

shuffled_df = df.reindex(np.random.permutation(df.index))

After I did that, I started getting similar results between .score and cross_val_score:

R2-dot-score: 0.9910715555903232
R2-cross-val: [0.99265184 0.9923142  0.9922923  0.99259524 0.99195022]

Answer 1

OK, I may have solved this. It seems as if cross_val_score does not randomize the data, which may be leading to worse predictions when similar data is grouped together. The easiest solution I found (via this answer ) to this was to simply shuffle the dataframe before running the model:

shuffled_df = df.reindex(np.random.permutation(df.index))

After I did that, I started getting similar results between .score and cross_val_score:

R2-dot-score: 0.9910715555903232
R2-cross-val: [0.99265184 0.9923142  0.9922923  0.99259524 0.99195022]