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在学习使用 Pipelines 和 GridSearchCV 时，我尝试将随机森林回归器与支持向量回归器集成在一起。单独 GridSearchCV 把两者都放在大约 90% 的分数，我是不是很卡住。但将 SVR 置于随机森林之前，它跃升至 92%。

我找不到任何这样的例子，所以我认为它不是很有用，不正确，或者有更好的方法。将不胜感激任何指导。

我使用带有套索和随机森林的 SKLearn 波士顿房屋集创建了一个快速示例。合并使“mean_test_score”从大约 62% 增加到 65%。相关片段如下，完整笔记本位于： http: //nbviewer.jupyter.org/gist/Blebg/ce279345456dc706d2deddcfab49a984

class Lasso_t(Lasso): #Give Lasso a transform function

    def transform(self, x):
        return super(Lasso_t, self).predict(x).reshape(-1, 1)


#The pipe creates a Lasso regression prediction that Random Forest gets as a variable
pipe = Pipeline(steps = [
    ('std_scaler', StandardScaler()),
    ('union', FeatureUnion([('reg', Lasso_t(alpha = 0.2)),
                            ('keep_X', FunctionTransformer(lambda x : x))])),
    ('rf', RandomForestRegressor(n_estimators = 100))]) 

params = dict(
    rf__min_samples_leaf = [1,5,10],
    rf__max_features = ['log2','sqrt'])

grid_search = GridSearchCV(pipe, param_grid=params, cv = 5)
grid_search.fit(X,y)
pd.DataFrame(grid_search.cv_results_).sort_values(by = 'rank_test_score').head(3)
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1 回答 1

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您可能会寻找sklearn.ensemble.VotingRegressor可以通过取平均值来组合两个回归模型的方法。

这是一个帮助您入门的示例：

from sklearn.datasets        import make_regression
from sklearn.ensemble        import RandomForestRegressor, VotingRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.pipeline        import Pipeline
from sklearn.preprocessing   import StandardScaler
from sklearn.svm             import SVR

# Make fake data     
X, y = make_regression(n_samples=1_000, n_features=20, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=42)

pipe = Pipeline([('scl', StandardScaler()),
                 ('vr', VotingRegressor([('svr', SVR()), ('rfr', RandomForestRegressor())]))
                ])

search_space = [{'vr__rfr__min_samples_leaf': [1, 5, 10]}]

gs_cv = GridSearchCV(estimator=pipe,
                     param_grid=search_space,
                     n_jobs=-1)

gs_cv.fit(X_train, y_train)
gs_cv.predict(X_test)
于 2021-02-24T20:06:47.980 回答