Python构建lassocv模型并输出Rad-score公式
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Python构建lassocv模型并输出Rad-score公式
\'signature = wavelet-HLH_glszm_GrayLevelVariance*-0.03068740097987764 + wavelet-HHL_glcm_Correlation*-0.08710971368430308 + wavelet-HHL_firstorder_Median*0.035165424725141704 + wavelet-HLL_gldm_LargeDependenceHighGrayLevelEmphasis*0.008126356021741077 + wavelet-LLH_gldm_DependenceVariance*-0.03616063230604407 + wavelet-LHL_glcm_Correlation*-0.07247491830055819\'
# 构建lassocv模型
alphas = np.logspace(-10, 1, 100, base = 10)
# Create lasso regression with three alpha values
regr_cv = LassoCV(alphas=alphas, cv = 5, max_iter = 1e4,selection = \'random\',random_state=42)
#regr_cv = LassoCV(alphas
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