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What is Logistic Regression ?
Logistic regression is a powerful supervised machine learning algorithm. It is used for binary classification problems. Logistic regression uses logistic functions. The main distinction between linear and logistic regression is that the range of logistic regression is limited to 0 and 1. Furthermore, logistic regression does not require a linear relationship between input and output variables, unlike linear regression. The weights for the features are approximately represented by the logistic regression model parameters. The S-shaped logistic function maps each weighted feature vector to a value between 0 and 1. This result is interpreted as the likelihood that a given example belongs to a specific class. The learning algorithm tunes the weights in order to correctly classify the training examples.
Type of Logistic Regression
Binomial Logistic Regression
Multinomial Logistic Regression
Ordinal Logistic Regression
Sigmoid function
It is the logistic expression is used for logistic regression
Sigmoid function change any line into curve which has discrete values like 0 and 1
A solution for classification is logistic regression. Instead of fitting a straight line or hyperplane, the logistic regression model uses the logistic function to squeeze the output of a linear equation between 0 and 1.
Logistic function is defined as :
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