Polynomial & Logistic Regression;Artificial Intelligence
Regression techniques for students and professionals. Learn Polynomial & Logistic Regression and code them in python
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.
Polynomial Regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in X. Polynomial regression fits a nonlinear relationship between the value of X and the corresponding conditional mean of Y. denoted E(y |x), and has been used to describe nonlinear phenomena such as the growth rate of tissues, the distribution of carbon isotopes in lake sediments, and the progression of disease epidemics. Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, Polynomial Regression is considered to be a special case of multiple linear regression.
The predictors resulting from the polynomial expansion of the "baseline" predictors are known as interaction features. Such predictors/features are also used in classification settings.
In this Course you learn Polynomial Regression & Logistic Regression You learn how to estimate output of nonlinear system by Polynomial Regressions to find the possible future output Next you go further You will learn how to classify output of model by using Logistic Regression
In the first section you learn how to use python to estimate output of your system. In this section you can estimate output of:
- Nonlinear Sine Function
- Python Dataset
- Temperature and CO2
In the Second section you learn how to use python to classify output of your system with nonlinear structure .In this section you can estimate output of:
- Classify Blobs
- Classify IRIS Flowers
- Classify Handwritten Digits
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StartIntroduction and Outline (7:22)
StartPolynomial Regression Sine Function Part-1 (4:33)
StartPolynomial Regression Sine Function Part-2 (7:29)
StartPolynomial Regression Built-in Dataset Part-1 (8:24)
StartPolynomial Regression Built-in Dataset Part-2 (5:56)
StartPolynomial Regression Built-in Dataset Part-3 (5:59)
StartPolynomial Regression CO2vsTemp part-1 (3:28)
StartPolynomial Regression CO2vsTemp part 2 (7:52)
StartPolynomial Regression CO2vsTemp part-3 (1:33)
StartLogistic Regression Theory (4:45)
StartLogistic Regression for Blobs Datasets part-1 (11:57)
StartLogistic Regression for Blobs Datasets part-2 (4:28)
StartLogistic Regression for Blobs Datasets part-3 (4:05)
StartLogistic Regression for IRIS Flowers (4:22)
StartLogistic Regression Handwritten Digits (14:49)