在管道中定义一个步骤或在 param_grid 中定义一个步骤之间的区别，例如降维

Question

在这个 scikit-learn 文档中：

https://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py

我无法弄清楚在 pipe 和 param_grid 中进行降维的目的；换句话说，如果在 pipe 或 param_grid 中定义所有降维，会发生什么？代码如下：

pipe = Pipeline(  
    [
        # the reduce_dim stage is populated by the param_grid
        ("reduce_dim", "passthrough"),
        ("classify", LinearSVC(dual=False, max_iter=10000)),
    ]
)  

N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [
    {
        "reduce_dim": [PCA(iterated_power=7), NMF()],
        "reduce_dim__n_components": N_FEATURES_OPTIONS,
        "classify__C": C_OPTIONS,
    },
    {
        "reduce_dim": [SelectKBest(chi2)],
        "reduce_dim__k": N_FEATURES_OPTIONS,
        "classify__C": C_OPTIONS,
    },
]
reducer_labels = ["PCA", "NMF", "KBest(chi2)"]

grid = GridSearchCV(pipe, n_jobs=1, param_grid=param_grid)
X, y = load_digits(return_X_y=True)
grid.fit(X, y)  

Answer 1

它需要在param_grid 中，因为您想尝试不同的选项。

它还需要在pipeline 中，否则您在给定时间点尝试的选项根本不会被使用。