REGRESSION IN MACHINE LEARNING
Regression in machine learning is a technique used to predict numerical values based on previous data. It is often used to find the relationship between two or more variables. For example, if you want to predict the price of a house based on its size, location, and number of bedrooms, you can use regression.
Regression works by analyzing the relationship between the input variables (also known as independent variables) and the output variable (also known as dependent variable). The goal is to find a mathematical formula that best describes this relationship.
Once the formula is found, you can use it to predict the value of the output variable based on the input variables. Regression can be used for simple problems with just one input variable, or more complex problems with multiple input variables.
Overall, regression is a powerful tool that can be used in many different fields, including finance, healthcare, and marketing, to name a few.
Simple Linear Regression
The simple linear regression equation is:
Y= 𝝱0 + 𝝱1x+ε
Graph of the regression equation is a straight line.
𝝱0 is the y intercept of the regression line.
𝝱1 is the slope of the regression line.
Y is the expected value of y for a given x value.
Simple linear regression is a statistical technique used to establish a relationship between two variables, where one variable is the independent variable and the other is the dependent variable. In simple linear regression, we assume that there is a linear relationship between the independent variable and the dependent variable.
The goal of simple linear regression is to find a line that best fits the data points on a scatter plot. This line is called the regression line or the line of best fit. The regression line is characterized by its slope and y-intercept, which are calculated using statistical formulas.
The slope of the regression line represents the change in the dependent variable for every one-unit increase in the independent variable. The y-intercept represents the predicted value of the dependent variable when the independent variable is zero.
Once the regression line has been established, it can be used to make predictions about the dependent variable based on values of the independent variable. This can be useful for making forecasts or estimating future outcomes.
Overall, simple linear regression is a powerful statistical tool for exploring and understanding the relationship between two variables.
Multiple Linear Regression
Multiple linear regression is a statistical method used to analyze the relationship between two or more independent variables and a dependent variable. The aim is to find a linear equation that can best predict the value of the dependent variable based on the values of the independent variables.
For example, let's say we want to predict the price of a house based on its size, number of bedrooms, and location. The size, number of bedrooms, and location would be our independent variables, and the price would be our dependent variable.
To perform multiple linear regression, we collect data on the independent and dependent variables for a sample of houses. We then use statistical techniques to find the best fitting line, which will allow us to predict the price of a house based on its size, number of bedrooms, and location.
The line we find will have an intercept (the value of the dependent variable when all the independent variables are zero) and a slope for each independent variable (the change in the dependent variable for a one-unit change in the independent variable, holding all other independent variables constant).
Multiple linear regression can be a powerful tool for predicting outcomes, but it's important to remember that correlation does not equal causation. In other words, just because two variables are correlated does not mean that one causes the other. Careful analysis and consideration of other factors are necessary to draw meaningful conclusions from multiple linear regression.
Logistic Regression
Logistic regression is a statistical method used for predicting a binary outcome (e.g., yes/no or true/false) based on one or more predictor variables. It works by estimating the probability of the binary outcome using a logistic function, which maps the values of the predictor variables to a value between 0 and 1. The estimated probability is then used to classify the observation as either one of the binary outcomes.
For example, if we want to predict whether a student will pass or fail an exam based on their study hours and previous grades, we can use logistic regression to estimate the probability of passing given the values of these predictor variables. If the estimated probability is above a certain threshold (e.g., 0.5), we classify the student as a pass, otherwise, we classify them as a fail.
Logistic regression is widely used in many fields, including healthcare, finance, and marketing, to predict binary outcomes and make data-driven decisions.
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