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This article mainly introduces the meaning of python model integration, the article is very detailed, has a certain reference value, interested friends must read it!

Description

1. Model integration refers to the integration of the prediction results of a series of different models so as to obtain better prediction results.

2. The diversity of models is very important for model integration. Diversityisstrength. The model for integration should be as good as possible and as different as possible.

The same network, using different random initialization, multiple independent training, and then integration, does not make much sense. A better approach is to integrate models with very different structures, so that the deviations of each model cancel each other out in different directions, and the results are more stable and accurate.

Example

You can integrate them in many different ways, and the easiest way is to take an average:

Final_preds = 0.25 * (preds_a + preds_b + preds_c + preds_d)

Because there is a gap in the performance of each model, a better approach is weighted average:

Final_preds = 0. 5 * preds_a + 0. 25 * preds_b + 0. 1 * preds_c + 0. 15 * preds_d is all the content of the article "what does python Model Integration mean?" Thank you for reading! Hope to share the content to help you, more related knowledge, welcome to follow the industry information channel!

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