Logistic Regression in Machine Learning – A Basic Guide For 2021

Introduction

Logistic Regression is one of the most popular and useful Machine Learning classification algorithms. If you want to become an expert Machine Learning professional, you should get acquainted with Logistic Regression in Machine Learning. 

In this article, we shall understand Logistic Regression in Machine Learning. Let’s get started!

Table of Contents

1. What Is Logistic Regression?
2. Types Of Logistic Regression
3. Logistic Regression Assumptions
4. Logistic Regression Model
5. How Is Logistic Regression Used?

1) What Is Logistic Regression?

Logistic Regression is an algorithm for classification used when the target variable is categorical. The Logistic Regression algorithm comes into the picture when the data has a binary output belonging to one class, i.e., either 0 or 1. 

Mathematically, P(Y=1) is forecast as an X function by a Logistic Regression model. It is concerned with different classification issues, including spam detection, diabetes prediction, cancer sensing, etc. It is one of the basic ML-algorithms to be used.

2) Types Of Logistic Regression

Logistic Regression typically means Binary Logistic Regression with binary target variables. But it can be predicted by two additional types of destination variables. According to the number of these groups, the following types of Logistic Regression come into play – 

• Binary or Binomial – A dependent variable can only have two possible forms, 1 and 0, in this form of classification. These variables may, for example, indicate success or failure, win or lose or otherwise.

• Multinomial – The dependent variables may have three or more unordered types in such a category or the classes have no quantitative value. 

• Ordinal – Dependent variables can have three or more ordered kinds or forms of quantitative importance in this classification form. This, for example, can include ‘bad’ and ‘good’ variables,’ ‘excellent,’ which can have scores of 0,1,2,3 for each group.

3) Logistic Regression Assumptions

We must know the following assumptions about Logistic Regression before we plunge into the applications of Logistic Regression − 

• In the event of Binary Logistic Regression, the target variables should always be binary, and the optimal result should be the amount of factor 1. 
• No multi-linearity can exist in the model, so the different variables must be separate from each other. 
• In our model, we have to include substantial variables. 
• For Logistic Regression, we can choose a broad sampled scale.

4) Logistic Regression Model

• Binary Logistic Regression Model – 

Binary or Binomial Logistic Regression in which the target or dependent variable may have either two types, 1 or 0, is the simplest form of Logistic Regression. It helps us to model a relationship with a binary/ binomial target variable between several predictor variables. In Logistic Regression, the linear function is primarily used in the following relation as an input to another function, like g.

g is the logistic or sigmoid function presented as follows –

With the support of the following graph, you will be able to see the sigmoid curve. You will see y-axis values ranging from 0 to 1 and touching the 0.5 axis.

The groups can be classified into positive or negative classes. The outcome is likely to be positive if it varies from 0 to 1. To reach our goals, the hypothesis’s effects are viewed as positive, whether they are 0.5 or negative.

• Multinomial Logistic Regression Model – 

Multinomial Logistic Regressions in which the target or dependent variable may have three or more unordered forms are a successful form of logistic Regression, i.e., types with no quantitative significance.

5) How Is Logistic Regression Used?

It is essential to understand that Logistic Regression should be applied only when target variables fall into discrete categories. We should not use Logistic Regression when there are continuous values of the target value. Examples of Logistic Regression situations include – 

• Predict whether or not an email is a spam.
• Whether a tumour is malignant or not? 
• Is it a toxic or consumable mushroom?

Using Regression, we typically define a threshold, indicating the example’s significance in one class vs. the other. A threshold of 0.5 can be set in spam classification, which will lead to a 50 percent or more risk of being spammed and any email with less than 50 percent chance being classed as ‘non-spam’ email. 

While Logistic Regression is ideal for binary classification, it is possible to use it for multiple classification problems, three or more classifying tasks. You do this by applying a strategy of “one vs. all.”

Conclusion

Logistic Regression is a robust algorithm for Machine Learning that uses sigmoid functions and works best on binary classification issues, even though we can use the “one versus all” approach in multi-class classification issues.
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