python 回归问题特征筛选

wei_liao

于 2019-08-25 21:44:49 发布

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分类专栏： python

本文链接：https://blog.youkuaiyun.com/weixin_41358871/article/details/100068283

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本文探讨了在Python中解决回归问题时如何进行有效的特征筛选。内容包括使用相关系数矩阵、递归消除、基于惩罚的正则化方法以及特征重要性评估等策略，旨在提高模型的预测性能和降低过拟合风险。

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import statsmodels.formula.api as smf
import pandas as pd
 
def forward_selected(data, response):
    """前向逐步回归算法，源代码来自https://planspace.org/20150423-forward_selection_with_statsmodels/
    使用Adjusted R-squared来评判新加的参数是否提高回归中的统计显著性
    Linear model designed by forward selection.
    Parameters:
    -----------
    data : pandas DataFrame with all possible predictors and response
    response: string, name of response column in data
    Returns:
    --------
    model: an "optimal" fitted statsmodels linear model
           with an intercept
           selected by forward selection
           evaluated by adjusted R-squared
    """
    remaining = set(data.columns)
    remaining.remove(response)
    selected = []
    current_score, best_new_score = 0.0, 0.0
    while remaining and current_score == best_new_score:
        scores_with_candidates = []
        for candidate in remaining:
            formul

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