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Logistic Regression

This presentation guide you through Logistic Regression, Assumptions of Logistic Regression, Types of Logistic Regression, Binary Logistic Regression, Multinomial Logistic Regression and Ordinal Logistic Regression.<br><br>For more topic stay tuned with Learnbay.

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Logistic Regression

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  1. Logistic Regression Swipe

  2. What is Logistic Regression? Logistic regression is a statistical technique for describing and explaining the connection between one dependent binary variable and one or more nominal, ordinal, interval, or ratio- level independent variables.

  3. Assumptions of Logistic Regression Adequate sample size (too few participants for too many predictors is bad). Absence of multicollinearity (multicollinearity = high intercorrelations among the predictors). No outliers

  4. Types of Logistic Regression Binary Logistic Regression Multinomial Logistic Regression Ordinal Logistic Regression

  5. Binary Logistic Regression Based on the values of the independent variables, binary logistic regression is used to estimate the likelihood of being a case (predictors). The odds are calculated by dividing the chance that a given result is a case by the probability that it is not.

  6. Multinomial Logistic Regression Multinomial logistic regression is a classification technique that extends logistic regression to situations with more than two discrete outcomes. Three or more categories without ordering. Example: Predicting which food is preferred more (Veg, Non-Veg, Vegan)

  7. Ordinal Logistic Regression Ordinal Regression (sometimes called Ordinal Logistic Regression) is regression extension. a binomial logistic With independent variables, ordinal regression is used to predict the dependent variable. ‘ordered' multiple categories and

  8. Topics for next Post Naive bayes Linear Discriminant Analysis Decision tree Stay Tuned with

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