ML | Linear Regression vs Logistic Regression
Linear Regression is a machine learning algorithm based on supervised regression algorithm. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind of relationship between the dependent and independent variables, they are considering and the number of independent variables being used. Logistic regression is basically a supervised classification algorithm. In a classification problem, the target variable(or output), y, can take only discrete values for a given set of features(or inputs), X.
Sl.No. |
Linear Regression |
Logistic Regression |
1. |
Linear Regression is a supervised regression model. |
Logistic Regression is a supervised classification model. |
2. |
Equation of linear regression: y = a0 + a1x1 + a2x2 + … + aixi Here, y = response variable xi = ith predictor variable ai = average effect on y as xi increases by 1 |
Equation of logistic regression y(x) = e(a0 + a1x1 + a2x2 + … + aixi) / (1 + e(a0 + a1x1 + a2x2 + … + aixi)) Here, y = response variable xi = ith predictor variable ai = average effect on y as xi increases by 1 |
3. |
In Linear Regression, we predict the value by an integer number. |
In Logistic Regression, we predict the value by 1 or 0. |
4. |
Here no activation function is used. |
Here activation function is used to convert a linear regression equation to the logistic regression equation |
5. |
Here no threshold value is needed. |
Here a threshold value is added. |
6. |
Here we calculate Root Mean Square Error(RMSE) to predict the next weight value. |
Here we use precision to predict the next weight value. |
7. |
Here dependent variable should be numeric and the response variable is continuous to value. |
Here the dependent variable consists of only two categories. Logistic regression estimates the odds outcome of the dependent variable given a set of quantitative or categorical independent variables. |
8. |
It is based on the least square estimation. |
It is based on maximum likelihood estimation. |
9. |
Here when we plot the training datasets, a straight line can be drawn that touches maximum plots. |
Any change in the coefficient leads to a change in both the direction and the steepness of the logistic function. It means positive slopes result in an S-shaped curve and negative slopes result in a Z-shaped curve. |
10. |
Linear regression is used to estimate the dependent variable in case of a change in independent variables. For example, predict the price of houses. |
Whereas logistic regression is used to calculate the probability of an event. For example, classify if tissue is benign or malignant. |
11. |
Linear regression assumes the normal or gaussian distribution of the dependent variable. |
Logistic regression assumes the binomial distribution of the dependent variable. |
12. |
Applications of linear regression:
- Financial risk assessment
- Business insights
- Market analysis
|
Applications of logistic regression:
- Medicine
- Credit scoring
- Hotel Booking
- Gaming
- Text editing
|
Last Updated :
22 Apr, 2023
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