机器学习策略

指导数据收集和模型改善的算法(策略）

yes

no

yes

yes

no

no

yes

no

yes

no

yes

no

is the performance
on the training set
acceptable?

make some changes on your AI architecture:
e.g. using broader and wider ANN,
fine-tuning some important hyper-parameters,
such as learning rate and batch size

is performance
on the test set
acceptable?

is performance on
the training set better?

achieve
reliable model

can more high-quality data
be found
at an acceptable cost?

start over, collect
cleaner data or a richer set of feature
to improve the quality
of training data

collect more data

1.augment training set:
e.g.multi-task learning procedures
2.use special architectures and model regularization:
e.g. dropout

re-train model

is performance
on the test set
acceptable?

achieve reliable model

is collecting more
data practicable?

collect more data

improve the learning algorithm