小开

最佳答案

If you initialize the model with verbose=1 before calling fit you should get some kind of output indicating the progress.

For example sklearn.ensemble.GradientBoostingClassifer(verbose=1) provides progress output that looks like this:

  Iter       Train Loss   Remaining Time
1           1.2811            0.71s
2           1.2595            0.58s
3           1.2402            0.50s
4           1.2263            0.46s
5           1.2121            0.43s
6           1.1999            0.41s
7           1.1876            0.39s
8           1.1761            0.38s
9           1.1673            0.37s
10           1.1591            0.36s
20           1.1021            0.29s
30           1.0511            0.27s
40           1.0116            0.25s
50           0.9830            0.22s
60           0.9581            0.19s
70           0.9377            0.16s
80           0.9169            0.14s
90           0.9049            0.12s
100           0.8973            0.10s

小开

Many models support a verbose argument which gives progress (and sometimes an indication on the rate of convergence).

e.g.

clf = MLPClassifier(verbose=True)

(see MLPClassifier )

If you have a loop outside of the learning model, I recommend tqdm.

小开

Not all scikit-learn models support the verbose parameter

Unfortunately not all scikit-learn models allow the verbose parameter. Off the top of my head I can say these models do not allow verbose parameter (there may be more):

Yet curiously ExtraTreesClassifier which also belongs to sklearn.ensemble (just like AdaBoostClassifier), does allow it.

Looks like not all members of sklearn.ensemble share the same base properties.

一个为 scikit 学习的进度条？

Not all scikit-learn models support the verbose parameter