Linear Regression:
· Definition: Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data.
· Example: Suppose you want to predict the price of a house based on its size. In linear regression, you would use the size of the house (independent variable) to predict its price (dependent variable).
· Application: Linear regression is widely used in various fields such as finance (predicting stock prices based on historical data), economics (predicting consumer spending based on income), and healthcare (predicting patient outcomes based on medical data).
2. Polynomial Regression:
· Definition: Polynomial regression is an extension of linear regression where the relationship between the independent and dependent variables is modeled as an nth-degree polynomial.
· Example: Suppose you want to predict the trajectory of a projectile based on time. In polynomial regression, you might use a quadratic or cubic polynomial to model the trajectory accurately.
· Application: Polynomial regression is used in physics (modeling motion), environmental science (predicting temperature patterns), and engineering (predicting material behavior under varying conditions).
3. Logistic Regression:
· Definition: Logistic regression is a statistical method used for binary classification problems, where the output variable is categorical (e.g., yes/no, true/false) rather than continuous.
· Example: Suppose you want to predict whether an email is spam or not based on its content. In logistic regression, you would use features from the email (e.g., words, sender, subject) to predict the probability of it being spam.
· Application: Logistic regression is extensively used in spam detection, credit risk assessment (predicting default vs. non-default), healthcare (predicting disease presence based on symptoms), and marketing (predicting customer churn).
0 Comments