Scikit learn sklearn不遵循n_iter参数：给出比要求更多的迭代

以下是我有一段时间的疑问。如果它能引起你的共鸣，希望它能帮助你

我有以下简单的代码

with_model_analysis = Perceptron(n_iter=2, warm_start=True, verbose=1)

当运行以下代码时

with_model_analysis.fit(X_train, Y_train)

我得到的详细输出如下：

-- Epoch 1
Norm: 2117.10, NNZs: 151491, Bias: -0.200000, T: 2438128, Avg. loss: 0.136197
Total training time: 1.57 seconds.
-- Epoch 2
Norm: 2152.62, NNZs: 152310, Bias: -0.210000, T: 4876256, Avg. loss: 0.138114
Total training time: 3.14 seconds.
-- Epoch 1
Norm: 2864.00, NNZs: 144626, Bias: -0.250000, T: 2438128, Avg. loss: 0.140278
Total training time: 1.57 seconds.
-- Epoch 2
Norm: 2908.83, NNZs: 145051, Bias: -0.240000, T: 4876256, Avg. loss: 0.141844
Total training time: 3.13 seconds.
-- Epoch 1
Norm: 996.64, NNZs: 55420, Bias: -0.160000, T: 2438128, Avg. loss: 0.012540
Total training time: 1.59 seconds.
-- Epoch 2
Norm: 1013.77, NNZs: 56011, Bias: -0.150000, T: 4876256, Avg. loss: 0.012728
Total training time: 3.18 seconds.
-- Epoch 1
Norm: 2850.54, NNZs: 176581, Bias: -0.270000, T: 2438128, Avg. loss: 0.209191
Total training time: 1.58 seconds.
-- Epoch 2
Norm: 2895.90, NNZs: 177293, Bias: -0.260000, T: 4876256, Avg. loss: 0.211221
Total training time: 3.18 seconds.
-- Epoch 1
Norm: 1489.41, NNZs: 80787, Bias: -0.270000, T: 2438128, Avg. loss: 0.029082
Total training time: 1.54 seconds.
-- Epoch 2
Norm: 1516.51, NNZs: 81432, Bias: -0.290000, T: 4876256, Avg. loss: 0.029050
Total training time: 3.06 seconds.
-- Epoch 1
Norm: 2718.56, NNZs: 191107, Bias: 0.190000, T: 2438128, Avg. loss: 0.178792
Total training time: 1.48 seconds.
-- Epoch 2
Norm: 2762.41, NNZs: 191638, Bias: 0.220000, T: 4876256, Avg. loss: 0.181443
Total training time: 2.99 seconds.
[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:   28.5s finished

最后一行是什么意思？当所需的迭代次数只有2次时，为什么要进行6*2次迭代？

6表示输出类的数量。在多类分类中，它训练一个与其余决策边界，从而分别训练类