What’s the difference between Linear and Logistic Regression?
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Ajink Gupta Selected answer as best May 13, 2024
- Nature of Dependent Variable:
- Linear regression is used when the dependent variable is continuous while logistic regression is used when the dependent variable is binary (two categories).
- Output:
- In linear regression, the output is continuous and can take any value, while in logistic regression, the output is a probability between 0 and 1.
- Assumption:
- Linear regression assumes a linear relationship between the dependent and independent variables, while logistic regression does not assume linearity.
- Model:
- Linear regression uses a linear model to fit the data, while logistic regression uses a logistic function to model the probability of the dependent variable.
- Error Function:
- In linear regression, the error function is based on the difference between the predicted and actual values, while in logistic regression, the error function is based on the maximum likelihood estimation.
- Interpretation of Coefficients:
- In linear regression, the coefficients represent the change in the dependent variable associated with a one-unit change in the independent variable, while in logistic regression, the coefficients represent the change in the log-odds of the outcome associated with a one-unit change in the independent variable.
- Goodness of Fit:
- In linear regression, goodness of fit is often measured using metrics like R-squared, while in logistic regression, metrics like the likelihood ratio test or the AIC are used to assess model fit.
- Usage:
- Linear regression is commonly used for predicting continuous outcomes like sales, temperature, etc., while logistic regression is used for predicting binary outcomes like yes/no, pass/fail, etc.
- Outliers:
- Linear regression is sensitive to outliers, which can skew the results, whereas logistic regression is less affected by outliers due to the logistic function used.
- Decision Boundary:
- In logistic regression, there is a decision boundary that separates the two classes, while in linear regression, the line can extend beyond the range of the data points.
Ajink Gupta Selected answer as best May 13, 2024