Introduction to
Logistic Regression
Logistic regression is one of the most popular Machine Learning algorithms,
which comes under the Supervised Learning technique.
Logistic regression predicts the output of a categorical dependent variable.
Therefore the outcome must be a categorical or discrete value. It can be
either Yes or No, 0 or 1, true or False, etc. but instead of giving the exact
value as 0 and 1, it gives the probabilistic values which lie between 0 and 1.
Logistic regression is used for solving the classification problems.
, Types Of Logistic Regression:
Binomial: In binomial Logistic regression, there can be only two possible types of the dependent variable
Multinomial: In multinomial Logistic regression, there can be 3 or more possible unordered types of the d
“dogs”, or “sheep”
Ordinal: In ordinal Logistic regression, there can be 3 or more possible ordered types of dependent variab
“High”.
Logistic Regression
Logistic regression is one of the most popular Machine Learning algorithms,
which comes under the Supervised Learning technique.
Logistic regression predicts the output of a categorical dependent variable.
Therefore the outcome must be a categorical or discrete value. It can be
either Yes or No, 0 or 1, true or False, etc. but instead of giving the exact
value as 0 and 1, it gives the probabilistic values which lie between 0 and 1.
Logistic regression is used for solving the classification problems.
, Types Of Logistic Regression:
Binomial: In binomial Logistic regression, there can be only two possible types of the dependent variable
Multinomial: In multinomial Logistic regression, there can be 3 or more possible unordered types of the d
“dogs”, or “sheep”
Ordinal: In ordinal Logistic regression, there can be 3 or more possible ordered types of dependent variab
“High”.
