Predictive Modeling
Predictive Modeling
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logistic regression analytics vidhya
logistic regression analytics vidhya Logistic Regression is a statistical method that we use to fit a regression model when the response variable is binary. In simple words, it predicts a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables. Logistic Regression is an estimation of Logit function. Logit function is simply a log of odds in favor of the event. This function creates a s-shaped curve with the probability estimate, which is very similar to the required step wise function Logistic Regression is a powerful tool for analyzing a data set when we want to predict a binary outcome. But it’s not limited to this; it can also be used when the outcome is a multi-class categorical variable. There are many advantages of using Logistic Regression. Some of the advantages are: The outcome is binary: Logistic regression is best used for predicting a binary outcome. Linearity of logit for predictors: The logit transformation used in logistic regression is linear...
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