Why logistic regression is called regression and not classification?

Logistic Regression is one of the basic and popular algorithms to solve a classification problem. It is named 'Logistic Regression' because its underlying technique is quite the same as Linear Regression. The term “Logistic” is taken from the Logit function that is used in this method of classification.

Why is logistic regression not classification?

It is only a classification algorithm in combination with a decision rule that makes dichotomous the predicted probabilities of the outcome. Logistic regression is a regression model because it estimates the probability of class membership as a (transformation of a) multilinear function of the features.

Why is logistic regression called regression and not logistic classification?

Linear regression gives a continuous value of output y for a given input X. Whereas, logistic regression gives a continuous value of P(Y=1) for a given input X, which is later converted to Y=0 or Y=1 based on a threshold value. That's the reason, logistic regression has “Regression” in its name.

Is logistic regression a classification or regression?

Logistic Regression is a classification technique used in machine learning. It uses a logistic function to model the dependent variable.

Is logistic regression for regression problems or classification problems?

Logistic Regression is a statistical approach and a Machine Learning algorithm that is used for classification problems and is based on the concept of probability.

Why Logistic Regression is a Linear Model?

Is logistic regression only used for classification?

Conclusion: Logistic regression is used for binary or multi-class classification, and the target variable always has to be categorical.

Why do we prefer logistic regression over Linear Regression for classification problems?

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.

What is difference between regression and classification?

The most significant difference between regression vs classification is that while regression helps predict a continuous quantity, classification predicts discrete class labels. There are also some overlaps between the two types of machine learning algorithms.

Why is logistic regression called Linear Regression?

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.)

What is regression and classification?

Classification is the task of predicting a discrete class label. Regression is the task of predicting a continuous quantity.

Why Linear Regression is not suitable for classification?

There are two things that explain why Linear Regression is not suitable for classification. The first one is that Linear Regression deals with continuous values whereas classification problems mandate discrete values. The second problem is regarding the shift in threshold value when new data points are added.

Why is logistic called logistic?

Logistic comes from the Greek logistikos (computational). In the 1700's, logarithmic and logistic were synonymous. Since computation is needed to predict the supplies an army requires, logistics has come to be also used for the movement and supply of troops".

How can logistic regression be used for regression and classification?

The basis of logistic regression is the logistic function, also called the sigmoid function, which takes in any real valued number and maps it to a value between 0 and 1. Logistic regression model takes a linear equation as input and use logistic function and log odds to perform a binary classification task.

What are the 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.

Is logistic regression a non linear classifier?

Logistic regression is neither linear nor is it a classifier. The idea of a "decision boundary" has little to do with logistic regression, which is instead a direct probability estimation method that separates predictions from decision.

Can logistic regression be used for non linear classification?

So to answer your question, Logistic regression is indeed non linear in terms of Odds and Probability, however it is linear in terms of Log Odds.

What is the difference between regression and classification provide an example?

The key distinction between Classification vs Regression algorithms is Regression algorithms are used to determine continuous values such as price, income, age, etc. and Classification algorithms are used to forecast or classify the distinct values such as Real or False, Male or Female, Spam or Not Spam, etc.

What is logistic regression classification?

Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. The nature of target or dependent variable is dichotomous, which means there would be only two possible classes.

What is the main difference between regression and classification trees?

The primary difference between classification and regression decision trees is that, the classification decision trees are built with unordered values with dependent variables. The regression decision trees take ordered values with continuous values.

Why is logistic regression better?

Logistic regression is easier to implement, interpret, and very efficient to train. If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting. It makes no assumptions about distributions of classes in feature space.

Can you use linear regression for classification?

You can apply linear regression for classification by assigning a threshold, given below is an example from an online course by Andrew NG where he fitted a line to the data set and used . 5 as threshold for classification. Although you should try to look at other classification techniques.

Is logistic regression mainly used for regression?

2) True-False: Is Logistic regression mainly used for Regression? Logistic regression is a classification algorithm, don't confuse with the name regression.

Why logistic regression has regression word in the name despite logistic regression is used as one of the classification algorithm?

Logistic Regression is one of the basic and popular algorithms to solve a classification problem. It is named 'Logistic Regression' because its underlying technique is quite the same as Linear Regression. The term “Logistic” is taken from the Logit function that is used in this method of classification.

What is the main purpose of logistic regression Do you know other regression that can provide similar estimates?

Logistic regression works by measuring the relationship between the dependent variable (what we want to predict) and one or more independent variables (the features). It does this by estimating the probabilities with the help of its underlying logistic function.

Why is logistic regression used?

Similar to linear regression, logistic regression is also used to estimate the relationship between a dependent variable and one or more independent variables, but it is used to make a prediction about a categorical variable versus a continuous one.