Logistic-Regression | basic implementation of Logistic Regression | Machine Learning library

As a continuation from this question, I want to run many logistic regression equations at once and then note if a group was significantly different from a reference group. This solution works, but it only works when I'm not missing values. Being that my data has 100 equations, it's bound to have missing values, so rather than this solution failing when it hits an error, how can I program it to skip the instances that throw an error?

Here's a modified dataset that's missing cases:

I am not an expert on logistic regression, but I thought when solving it using lgfgs it was doing optimization, finding local minima for the objective function. But every time I run it using scikit-learn, it is returning the same results, even when I feed it a different random state.

Below is code that reproduces my issue.

I am trying to understand how this function works and the mathematics behind it. Does decision_function() in scikitlearn give us log odds? The function return values ranging from minus infinity to infinity and it seems like 0 is the threshold for prediction when we are using decision_function() whereas the threshold is 0.5 when we are using predict_proba(). This is exactly the relationship between probability and log odds Geeksforgeeks.

I couldn't see anything about that in the documentation but the function behaves like log-likelihood I think. am I right?

As the title suggests. I'm playing around with a Twitter bot that scrapes rss feeds and tweets the title of the article and a link.

For some reason when I run the below code it runs without errors but doesn't retrieve the url link. Any suggestions are gratefully recieved.

Background and my thought process:

I wanted to see if I could utilize logistic regression to create a hypothesis function that could predict recessions in the US economy by looking at a date and its corresponding leading economic indicators. Leading economic indicators are known to be good predictors of the economy.

To do this, I got data from OECD on the composite leading (economic) indicators from January, 1970 to July, 2021 in addition to finding when recessions occurred from 1970 to 2021. The formatted data that I use for training can be found further below.

Knowing the relationship between a recession and the Date/LEI wouldn't be a simple linear relationship, I decided to make more parameters for each datapoint so I could fit a polynominal equation to the data. Thus, each datapoint has the following parameters: Date, LEI, LEI^2, LEI^3, LEI^4, and LEI^5.

The Problem:

When I attempt to train my hypothesis function, I get a very strange cost history that seems to indicate that I either did not implement my cost function correctly or that my gradient descent was implemented incorrectly. Below is the imagine of my cost history:

I have tried implementing the suggestions from this post to fix my cost history, as originally I had the same NaN and Inf issues described in the post. While the suggestions helped me fix the NaN and Inf issues, I couldn't find anything to help me fix my cost function once it started oscillating. Some of the other fixes I've tried are adjusting the learning rate, double checking my cost and gradient descent, and introducing more parameters for datapoints (to see if a higher-degree polynominal equation would help).

My Code The main file is predictor.m.

I am trying code from this page. I ran up to the part LR (tf-idf) and got the similar results

After that I decided to try GridSearchCV. My questions below:

1)

I am trying to fit the Doc2Vec method in a dataframe which the first column has the texts, and the second one the label (author). I have found this article https://towardsdatascience.com/multi-class-text-classification-with-doc2vec-logistic-regression-9da9947b43f4, which is really helpful. However, I am stuck at how to build a model

I want to try all regression algorithms on my dataset and choose a best. I decide to start from Linear Regression. But i get some error. I tried to do scaling but also get another error.

Here is my code:

I am using Python to predict values and getting many warnings like:

Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( C:\Users\ASMGX\anaconda3\lib\site-packages\sklearn\linear_model_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

this prevents me from seeing the my own printed results.

Is there any way I can stop these warnings from showing?