数据挖掘 - 在 python 中绘制逻辑回归的 L2 正则化路径的优雅方法？ - 吾爱随笔录

尝试使用以下代码绘制逻辑回归的 L2 正则化路径（正则化路径示例可在 ML 教科书 Elements of Statistical Learning https://web.stanford.edu/~hastie/Papers/ESLII的第 65 页找到.pdf）。有一种感觉，我正在以愚蠢的方式做这件事 - 认为有一种更简单，更优雅的编码方式 - 非常感谢建议。

counter = 0
for c in np.arange(-10, 2, dtype=np.float):    
    lr = LogisticRegression(C = 10**c,
                            fit_intercept=True,
                            solver = 'liblinear',
                            penalty = 'l2',
                            tol = 0.0001,
                            n_jobs = -1,
                            verbose = -1,
                            random_state = 0
                           )
    model=lr.fit(X_train_z, y_train)


    coeff_list=model.coef_.ravel()
    
    if counter == 0:
        coeff_table = pd.DataFrame(pd.Series(coeff_list,index=X_train.columns),columns=[10**c])
    else:
        temp_table = pd.DataFrame(pd.Series(coeff_list,index=X_train.columns),columns=[10**c])
        coeff_table = coeff_table.join(temp_table,how='left')
    counter += 1

plt.rcParams["figure.figsize"] = (20,10)
coeff_table.transpose().iloc[:,:10].plot()
plt.ylabel('weight coefficient')
plt.xlabel('C')
plt.legend(loc='right')
plt.xscale('log')
plt.show()

# This needs to be instantiated outside the loop so we don't start from scratch each time. lr = LogisticRegression(C = 1, # we'll override this in the loop warm_start=True, fit_intercept=True, solver = 'liblinear', penalty = 'l2', tol = 0.0001, n_jobs = -1, verbose = -1, random_state = 0 ) for c in np.arange(-10, 2, dtype=np.float): lr.set_params(C=10**c) model=lr.fit(X_train_z, y_train) ...