c122.txt

idx_cls acts as the column while i acts as the rows.

A solver is a piece of mathematical software, possibly in the form 
of a stand-alone computer program or as a software library, that 
'solves' a mathematical problem.

The solver orchestrates model optimization by coordinating the 
network's forward inference and backward gradients to form parameter 
updates that attempt to improve the loss.


Multinomial logistic regression is a simple extension of binary 
logistic regression that allows for more than two categories of 
the dependent or outcome variable.

Multinomial logistic regression is used when you have a categorical 
dependent variable with two or more unordered levels (i.e. two or 
more discrete outcomes). It is practically identical to logistic 
regression, except that you have multiple possible outcomes instead 
of just one.

For example, children’s food choices are influenced by their parents’ 
choices and the children’s pastimes (e.g. sports enthusiast vs. gamer).
 You could study the relationship between a child’s food choices with 
their parents’ choices and children’s pastimes. The dependent variable 
levels would be the different food choices (fast food, healthy choices,
 protein packed, vegan etc.). Or you might study how workers’ education
 levels and time on the job affect promotions. The independent variable
s would be education levels and time on the job, and the levels of the 
dependent variable might be promotion to team-leader roles, sales 
positions, or management positions.

The solvers implemented in the class Logistic Regression are 
“liblinear”, “newton-cg”, “lbfgs”, “sag” and “saga”