scores = cross_val_score(model,train_x,train_y,cv=5,scoring='neg_mean_squared_error')
cv或者grid_search的惯例是，会令scoring尽可能大，因为一般score是准确率这种越大越好的，而不是mse这种越小越好的。
所以mse=-neg_mean_squared_error
rmse =(-neg_mean_squared_error)**0.5
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