0, 0.1], max_features=[2_000, 5_000], for k, v in tuned_model.best_params_.items():
print(f"{v} <> {tuned_model.best_estimator因此,tuned_model.best_params_似乎是我所期望
首先用out.best_fit绘制了拟合函数,并用out.best_values.param给出了最优参数。absPlot[50:170,1], x=absPlot[50:170,0], method='nelder')
#calculate y-values of fit function using out.best_values.getyValTest = VoigtConv(absPlot[:,0], result.best_values.get('A'), result