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Machine learning is one of the majorly advancing technologies in today’s data-driven world. Many businesses survey their audience and gather loads and loads of data to make conclusions out of it. And they use advanced data science tools for the prediction process.
Linear regressions and logistic regression are the two most famous and commonly used algorithms when it comes to machine learning. Both being supervised machine learning algorithms, they serve different purposes. Linear regression is used for predicting continuous values, whereas logistic regression is used in the binary classification of values.
In this article, we will have a look at how the two are different from each other. First, let’s begin by defining the two.
A supervised machine learning algorithm linear regression assumes the presence of a linear relationship between independent and dependent variables. Linear regression is used to predict value based on the independent variable.
Logistic regression is also a supervised machine learning algorithm. However, the point of difference is that it is a classification algorithm. Logistic regression uses the value of the independent variable to predict the category of the dependent variable.
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The primary difference between linear and logistic regression lies in their application: linear for continuous predictions, logistic for classification.
Feature | Linear regression | Logistic regression |
Definition | A supervised learning technique for solving regression problems | A supervised learning technique for mainly used for classification problems |
Use | Used for predicting continuous dependent values with the help of independent variables | Used for binary classification or separation of discreet dependent values with the help of independent variables |
Output | Output can only be continuous values such as age, height, time, price, salary, etc. | The output can only be between 0 and 1. |
Graphical output | We find a best fit linear line which will predict the next value or variable | We find a s-curve or sigmoid curve which classify the variables |
Estimation accuracy | Least square method | Maximum likelihood estimation method |
Variable relationship | Relationship between dependent and independent variable should be linear | Relationship between dependent and independent variable is not required |
Collinearity | Collinearity between independent variables is allowed | Collinearity between independent variables is not allowed |
Applications | Used in businesses and forecasting stocks | Used in classification and image processing |
When deciding between logistic regression vs linear regression, consider whether your outcome variable is categorical or continuous.
[Related Read: Logistics Regression Assumption]
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Linear regression is a machine learning algorithm used to predict the output variable values based on the input variable values.
The x variables are the independent input variables and y are the dependent output variables. The value of y variables depends on the value of x variables.
Linear regression works by defining the relationship between input and output variables. It draws a line that plots the input data and maps it to the output data. This line is the line of best fir and is a mathematical representation of the relationship between the independent variables. The line is meant to cover as many input variables as possible and the left out variables are the outliers or noise.
The regression line is written as:
y= a0 + a1x + e
a0 is t intercept |
a1 is the slope of the line |
y is a dependent output variable |
x is an independent input variable |
e is the error term |
Example:
The above graph shows the experience as the input variable and salary as the output variable. Hence, it means that as your experience grows, your salary is bound to grow too. This way, through linear regression you can predict how much will be your approximate salary when you will have 11 years of experience.
Logistic regression in machine learning is used to predict the category of the dependent variable based on the independent variable with the output as 0 or 1.
The input data already belongs to a category, which means multiple input values can map to one output value. We use logistic regression to predict which category will the new input value belong. Hence the input is mapped into either 0 or 1.
Once the curve is drawn, showing the data mapping to the output, we need a line to separate these two outputs clearly. This line is called the threshold or the value at which this line is drawn is called the threshold value.
The equation for logistic regression is given by:
Example:
Let’s say you have a list of employee IDs and you want to bifurcate the IDs based on legitimate and fraudulent. In such cases, you will use logistic regression.
There are very few similarities between the two regression models. The two algorithms used in predictive analysis differ but they have similarities. Here is what they share;
1. Supervised machine learning algorithms
Both linear and logistic regression are available as algorithms under supervised machine learning which define them as such since they require labeled data for training. These models rely on input-output pairs before going ahead to ensure that their predictions are correct using unseen data.
2. Use of Linear Equations:
Both models use linear equations as part of the methods they employ. In linear regression, the output value is directly predicted by the equation. Logistic regression applies the linear equation in a logistic function such as to predict probabilities.
3. Parameter Estimation:
Both types of regression need to estimate parameters (coefficients) which specify the association between predictor variables and response variables. The main objective here is to identify the model that fits the data well.
4. Model Interpretation:
The coefficients that are output by both models can be understood as so that the effects of all variables affecting the dependent variable are made clear. Understanding relationships within datasets is facilitated by this ability to explain the meaning behind what each variable represents
5. Data requirements:
For both methods to learn well how best a model might generalize to unseen data, a lot f information is needed. Both of the methods are affected by how much data for training is available and its quality.
6. Assumptions:
Both models rely on there being some relationship between the dependent variable and independent variables. In the case of linear regression, this means that the relationship is linear. On the other hand, in logistic regression the relationship is described using a logistic function, albeit that linearity is applicable only to log odds.
Depending on what you’re trying to predict, there are many factors that will influence whether you should use a Linear vs Logistic Regression.
This sums up the differences between Linear Regression and Logistic Regression. While linear regression can help you predict the price of a car or an apartment, logistic regression can classify whether a mole in a body is benign or malignant.
Both the regression model can be used to make informed decisions. To know more about how you can use machine learning to predict outcomes or classify elements you can contact us.
This is the big difference between Linear Regression and Logistic Regression. Linear regression is used to predict values that vary over a continuous range, whereas logistic regression is employed when classifying binaries.
In case the dependent variable is categorical particularly for binary results for instance spam email against the non spam ones.
No, each has its own purpose; for instance linear regression is used when the outcome is not categorical while logistic regression is perfect for categorical outcome categories.
You want to use multiple linear regression whenever there are more than one predictor variable but still produce continuous predictions (e.g., house prices). Logistic regression, on the other hand, is more suitable when there are two categories (e.g., yes or no decisions in purchasing) with multiple predictors behind these choices.
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