我正在训练具有约70个功能的随机森林模型。经过10倍交叉验证后，我的平均R平方值为37 ...这没有意义。我将代码粘贴在下面，任何人都可以帮忙吗？
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators=50, random_state=1)
rf.fit(X, y)
print('training R2 = ' + str(round(rf.score(X, y), 3)))
print('training RMSE = %.3f' % np.sqrt(mean_squared_error(y_true=y, y_pred=rf.predict(X))))
# compute cross validation scores for random forest model
r2_scores = cross_val_score(rf, X, y, scoring='r2', cv=crossvalidation, n_jobs=-1)
scores = cross_val_score(rf, X, y, scoring='neg_mean_squared_error', cv=crossvalidation, n_jobs=-1)
rmse_scores = [np.sqrt(abs(s)) for s in scores]
print('Cross-validation results:')
print('Folds: %i, mean R2: %.3f' % (len(scores), np.mean(r2_scores)))
print('Folds: %i, mean RMSE: %.3f' % (len(scores), np.mean(np.abs(rmse_scores))))