SKILLSHARE ARTIFICIAL INTELLIGENCE ACADEMY 4 LOGISTIC REGRESSION ANALYSIS-iLLiTERATE

In statistics, Logistic Regression, or logit regression, or logit model is a regression model where the dependent variable (DV) is categorical. This article covers the case of a binary dependent variable—that is, where the output can take only two values, "0" and "1", which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick. Cases where the dependent variable has more than two outcome categories may be analysed in multinomial logistic regression, or, if the multiple categories are ordered, in ordinal logistic regression. In the terminology of economics, logistic regression is an example of a qualitative response/discrete choice model.

Logistic Regression was developed by statistician David Cox in 1958. The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). It allows one to say that the presence of a risk factor increases the odds of a given outcome by a specific factor.

In this course you learn how to classify datasets by by Logistic Regression to find the correct class for data and reduce error.In this course you learn how to use python to classify output of your system with nonlinear structure .In this section you can estimate output of:

- Blobs
- IRIS Flowers
- Handwritten Digits