Lets say we have a dependent variable $Y$ with few categories and set of independent variables.

What are the advantages of Multinomial logistic regression over set of binary logistic Regressions? By set of binary logistic regression I mean that for each category $y_{i} \in Y$ we build separate binary logistic regression model with target=1 when $Y=y_{i}$ and 0 otherwise.

Lets say we have a dependent variable $Y$ with few categories and set of independent variables.

What are the advantages of multinomial logistic regression over set of binary logistic regressions (i.e. one-vs-rest scheme)? By set of binary logistic regression I mean that for each category $y_{i} \in Y$ we build separate binary logistic regression model with target=1 when $Y=y_{i}$ and 0 otherwise.

Lets say we have a dependent variable $Y$ with few categories and set of independent variables.

What are the advantages of Multiple Logistic Regression over set of Binary Logistic Regressions? By set of binary logistic regression I mean that for each category $y_{i} \in Y$ we build separate binary logistic regression model with target=1 when $Y=y_{i}$ and 0 otherwise.

Lets say we have a dependent variable $Y$ with few categories and set of independent variables.

What are the advantages of Multinomial logistic regression over set of binary logistic Regressions? By set of binary logistic regression I mean that for each category $y_{i} \in Y$ we build separate binary logistic regression model with target=1 when $Y=y_{i}$ and 0 otherwise.