在同一模型上多次调用fit()有什么作用? [英] What does calling fit() multiple times on the same model do?

问题描述

实例化一个scikit模型(例如 LinearRegression )之后，如果我多次调用它的 fit()方法(使用不同的 X 和 y 数据)，会发生什么?是否像我刚刚重新实例化模型一样(例如，从头开始)将模型拟合到数据上，还是将先前调用 fit()时已经拟合的数据考虑在内?

尝试使用 LinearRegression (还要查看其源代码)，在我看来，每次调用 fit()时，它都会从头开始拟合，而忽略了以前对同一方法的任何调用.我不知道这通常是否正确，并且我可以在scikit learning的所有模型/管道中使用此行为.

解决方案

如果您第二次执行 model.fit(X_train，y_train)-它会覆盖所有先前拟合的系数，重量，截距(偏差)等.

如果您只想拟合一部分数据集，然后通过拟合新数据来改善模型，则可以使用 partial_fit()方法的对象)

After I instantiate a scikit model (e.g. LinearRegression), if I call its fit() method multiple times (with different X and y data), what happens? Does it fit the model on the data like if I just re-instantiated the model (i.e. from scratch), or does it keep into accounts data already fitted from the previous call to fit()?

Trying with LinearRegression (also looking at its source code) it seems to me that every time I call fit(), it fits from scratch, ignoring the result of any previous call to the same method. I wonder if this true in general, and I can rely on this behavior for all models/pipelines of scikit learn.

解决方案

If you will execute model.fit(X_train, y_train) for a second time - it'll overwrite all previously fitted coefficients, weights, intercept (bias), etc.

If you want to fit just a portion of your data set and then to improve your model by fitting a new data, then you can use estimators, supporting "Incremental learning" (those, that implement partial_fit() method)

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