# Why do we need binning in logistic regression?

Table of Contents

- 1 Why do we need binning in logistic regression?
- 2 Why use logistic regression and why not a linear regression for a classification problem?
- 3 What are the two main differences between logistic regression and linear regression?
- 4 Why is the logistic regression is considered linear?
- 5 Why binning is better than continuous variable?

## Why do we need binning in logistic regression?

Binning is widely used in credit scoring. In particular, it can be used to define the Weight of Evidence (WOE) transformation. In this paper, we first derive an explicit solution to a logistic regression model with one independent variable that has undergone a WOE transformation.

**Why is the loss function different in linear regression and logistic regression?**

Linear regression uses Least Squared Error as loss function that gives a convex graph and then we can complete the optimization by finding its vertex as global minimum. The loss function of logistic regression is doing this exactly which is called Logistic Loss .

**What are the benefits of using logistic regression instead of using linear regression?**

Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems. Linear regression provides a continuous output but Logistic regression provides discreet output.

### Why use logistic regression and why not a linear regression for a classification problem?

This article explains why logistic regression performs better than linear regression for classification problems, and 2 reasons why linear regression is not suitable: the predicted value is continuous, not probabilistic. sensitive to imbalance data when using linear regression for classification.

**Why is binning used?**

Data binning, also called discrete binning or bucketing, is a data pre-processing technique used to reduce the effects of minor observation errors. The original data values which fall into a given small interval, a bin, are replaced by a value representative of that interval, often the central value.

**Is binning necessary?**

With modern statistical methods it is generally not necessary to engage in binning, since anything that can be done on discretized “binned” data can generally be done on the underlying continuous values. The most common use of “binning” in statistics is in the construction of histograms.

#### What are the two main differences between logistic regression and linear regression?

Linear regression is used to predict the continuous dependent variable using a given set of independent variables. Logistic Regression is used to predict the categorical dependent variable using a given set of independent variables. Linear Regression is used for solving Regression problem.

**What loss function does logistic regression use?**

Log Loss is the loss function for logistic regression. Logistic regression is widely used by many practitioners.

**What are the main differences between logistic regression and linear regression?**

## Why is the logistic regression is considered linear?

The short answer is: Logistic regression is considered a generalized linear model because the outcome always depends on the sum of the inputs and parameters. Or in other words, the output cannot depend on the product (or quotient, etc.) of its parameters!

**What is binning technique?**

Binning method is used to smoothing data or to handle noisy data. In this method, the data is first sorted and then the sorted values are distributed into a number of buckets or bins. As binning methods consult the neighborhood of values, they perform local smoothing.

**Why is logistic regression not suitable for classification problems?**

This article explains why logistic regression performs better than linear regression for classification problems, and 2 reasons why linear regression is not suitable: the predicted value is continuous, not probabilistic. sensitive to imbalance data when using linear regression for classification. Supervised learning is an essential part

### Why binning is better than continuous variable?

All answers here are quite relevant. To add, in a world of large datasets there is a simple proof why binning might be better than continuous variable – those are models based on trees (specifically random forests and trees boosting).

**Is there a nonlinear hyperplane for logistic regression?**

Logistic regression has traditionally been used to come up with a hyperplane that separates the feature space into classes. But if we suspect that the decision boundary is nonlinear we may get better results by attempting some nonlinear functional forms for the logit function.

**What is the output of logistic regression?**

Logistic regression is used to predict the categorical dependent variable with the help of independent variables. The output of Logistic Regression problem can be only between the 0 and 1. Logistic regression can be used where the probabilities between two classes is required.