[英]How to read the fitting result of Random Forest ? sklearn
I mean the result, not the theory:我的意思是结果,而不是理论:
In linear regression, there is a formula to explain the variables and weights that contribute the final score.在线性回归中,有一个公式可以解释对最终得分有贡献的变量和权重。 In decision tree, there is a path map to explain what conditions result in the segmentation.
在决策树中,有一个路径图来解释什么条件会导致分割。
The only result I can read from < from sklearn.tree import DecisionTreeRegressor> is by pickle.dump.我可以从 < from sklearn.tree import DecisionTreeRegressor> 读取的唯一结果是 pickle.dump。 But pickle is still a black-box.
但是泡菜仍然是一个黑匣子。 Although features_importance_ output explains the weight importance of each features, however, that's an indirect method.
虽然 features_importance_ 输出解释了每个特征的权重重要性,但是,这是一种间接方法。 I still cannot understand how the score come from.
我还是不明白分数是怎么来的。
How read the data and explain the fitting result of Random Forest directly?如何读取数据,直接解释随机森林的拟合结果? Is there any formula or path map?
有公式或路径图吗?
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