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.

Why is Logistic regression best for classification?

Advantages of Logistic Regression

Logistic regression is easier to implement, interpret, and very efficient to train. It is very fast at classifying unknown records. It performs well when the dataset is linearly separable. It can interpret model coefficients as indicators of feature importance.

Why can't we use Linear Regression for the classification problem?

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.

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. It is used when the dependent variable (target) is categorical.

What is the difference between using Logistic regression for classification and using Logistic regression for predicting continuous values?

Linear regression is used for predicting the continuous dependent variable using a given set of independent features whereas Logistic Regression is used to predict the categorical. Linear regression is used to solve regression problems whereas logistic regression is used to solve classification problems.

Why Linear Regression is not suitable for Classification?

What is the difference between logistic regression and classification?

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

When should we use logistic regression?

Logistic regression is used when your Y variable can take only two values, and if the data is linearly separable, it is more efficient to classify it into two seperate classes.

Which regression is used for solving the classification problem?

Logistic regression is a simple yet very powerful algorithm to solve binary classification problems. The logistic function (i.e. sigmoid function) is also commonly used in very complex neural networks as the activation function of output layer.

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.

How logistic regression works well for classification algorithm?

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.

Can we solve classification problem with linear regression?

Yes! In this very simple dataset, logistic regression manages to classify all data points perfectly.

Why is logistic regression a type of classification technique and not a regression?

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.

Can we apply logistic regression on a 3 class classification problem explain your answer in less than two lines?

Solution: A

Yes, we can apply logistic regression on 3 classification problem, We can use One Vs all method for 3 class classification in logistic regression.

Why logistic regression is better for binary classification?

It is used when the data is linearly separable and the outcome is binary or dichotomous in nature. That means Logistic regression is usually used for Binary classification problems. Binary Classification refers to predicting the output variable that is discrete in two classes.

Can we use logistic regression for regression problems?

Logistic regression is one of the most popular Machine learning algorithm that comes under Supervised Learning techniques. It can be used for Classification as well as for Regression problems, but mainly used for Classification problems.

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

logistic regression or logit regression is a type of probabilistic statistical classification model. [1] It is also used to predict a binary response from a binary predictor, used for predicting the outcome of a categorical dependent variable (i.e., a class label) based on one or more predictor variables (features).

What are the main differences between logistic regression and Linear Regression?

Linear Regression is a supervised regression model. Logistic Regression is a supervised classification model. In Linear Regression, we predict the value by an integer number. In Logistic Regression, we predict the value by 1 or 0.

What are the advantages and disadvantages of logistic regression?

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.

Which type of problems are solved using logistic regression?

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.

Can logistic regression be used for multi-class classification problems?

Logistic regression, by default, is limited to two-class classification problems. Some extensions like one-vs-rest can allow logistic regression to be used for multi-class classification problems, although they require that the classification problem first be transformed into multiple binary classification problems.

Can logistic regression be applied to multi-class classification problem?

Multinomial logistic regression is a form of logistic regression used to predict a target variable have more than 2 classes. It is a modification of logistic regression using the softmax function instead of the sigmoid function the cross entropy loss function.

What type of data would you use with logistic regression?

Logistic Regression is used when the dependent variable(target) is categorical. For example, To predict whether an email is spam (1) or (0) Whether the tumor is malignant (1) or not (0)

Why regression is better than 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.

Which algorithm can be used for both classification and regression problems?

Decision Tree. The decision tree is one of the most popular machine learning algorithms used. They are used for both classification and regression problems.

When would you use regression analysis vs classification?

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.