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Logistic Regression
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Predicts the probability of the outcome class.
The algorithm finds the linear relationship between independent variables and a link function of these probabilities.
The link function that provides the best goodness-of-fit for the given data is chosen.
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The output from logistic regression will lie between 0 to 1.
The logistic regression curve is known as Sigmoid Curve.
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Probability values are segregated into binary outcomes using a Cutoff value. The default cutoff is treated as 0.5 (50%)
- If probability of an event > 0.5; then Event is considered to be True (predicted outcome = 1)
- If probability of an event
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The logistic regression performed using:
Where,
β0 = the y-axis intercept
βi = the model coefficient for the linear effect of variable is on y
e = the random error
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The probability function:
The output of the logistic regression will give a sigmoid curve (also known as S curve)
Interpretation: Probability p indicates that the event has a chance ‘p’ for a given input ‘x’.
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