I came across something new which I knew only sklearn used in statistical analysis just briefly. But there seems to have a given format for linear regression and logistic regression which analysis your given data frame. I would really appreciate if someone can explain me how this was created.

as the same has been used in the below notebook screenshot

I went through the link provided in the notebook which was really great seeing how all the plots created together, which tickled my mind to know the thought process behind this creative model as in R programming we had select a plot type for the data to be interpreted but this model will just create a complete regression analysis based on your data, which is just like wow.

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class=‘ovr’, n_jobs=1,
penalty=‘l2’, random_state=None, solver=‘lbfgs’, tol=0.0001,
verbose=0, warm_start=False)

Regards
DP

Could you add a screenshot or an specific description of the plot that you are interested in? Actually the Logistic regression only fits the numerical model. LBFGS is an optimisation algorithm for adjusting the weights of the model. So probably what you want to know is into the `utils.py` file, where we define all the helper functions for the plots and so on.

Hello Andres,

thank you for replying. I went through that LBFGS document via link provided in the notebook I shared screenshot, but some of the items I did not understand like below part mentioned in the notebook when I ran a cell

C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class=‘ovr’, n_jobs=1,
penalty=‘l2’, random_state=None, solver=‘lbfgs’, tol=0.0001,
verbose=0, warm_start=False)

what does n_jobs means? what is tol here, penalty here is regularization term which I understood. Also what is this intercept scaling. So that is why I wanted to know how this LBFGS was programmed or written.

Thank You
Regards
DP