Is logistic regression used for classification or regression?

Logistic Regression is a classification technique used in machine learning. It uses a logistic function to model the dependent variable. The dependent variable is dichotomous in nature, i.e. there could only be two possible classes (eg.: either the cancer is malignant or not).
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Can logistic regression be used for classification and regression?

Logistic regression is a simple yet very effective classification algorithm so it is commonly used for many binary classification tasks.
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Is logistic regression regression or classification?

Logistic regression is a classification algorithm used to assign observations to a discrete set of classes.
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Is logistic regression used only for classification?

Logistic regression is emphatically not a classification algorithm on its own. It is only a classification algorithm in combination with a decision rule that makes dichotomous the predicted probabilities of the outcome.
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Why is logistic regression used for 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.
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StatQuest: Logistic Regression



Why logistic regression is called regression and not 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.
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When should logistic regression be used?

Logistic Regression is another statistical analysis method borrowed by Machine Learning. It is used when our dependent variable is dichotomous or binary. It just means a variable that has only 2 outputs, for example, A person will survive this accident or not, The student will pass this exam or not.
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Where do you use 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)
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Is logistic regression used for clustering?

Background. Multilevel logistic regression models are widely used in health sciences research to account for clustering in multilevel data when estimating effects on subject binary outcomes of individual-level and cluster-level covariates.
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Can I use regression for classification?

As we all know, when we want to predict a continuous dependent variable from a number of independent variables, we used linear/polynomial regression. But when it comes to classification, we can't use that anymore. Fundamentally, classification is about predicting a label and regression is about predicting a quantity.
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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.
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Is logistic regression good for text classification?

More importantly, in the NLP world, it's generally accepted that Logistic Regression is a great starter algorithm for text related classification.
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Can we use logistic regression for multi class classification?

By default, logistic regression cannot be used for classification tasks that have more than two class labels, so-called multi-class classification. Instead, it requires modification to support multi-class classification problems.
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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.
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Why can't we use linear regression for classification?

Linear regression is a great algorithm but it is highly impacted by outliers. Hence we cannot use it to solve a classification problem. We need an algorithm that absorbs the effects of outliers without impacting the final output. Logistic regression does that by using something called a Sigmoid function.
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What is the main difference between regression and classification problem?

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.
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Is logistic regression quantitative or qualitative?

A least squares linear regression problem is used with a quantitative response whereas a logistic regression is used with a qualitative response (binary results between 0 and 1). We often use logistic regression for classification problems.
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Is logistic regression mainly used for regression True or false?

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

Logistic regression is usually used with binary response variables ( 0 or 1 ), the predictors can be continuous or discrete.
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Can logistic regression be used for prediction?

Logistic Regression is a Machine Learning method that is used to solve classification issues. It is a predictive analytic technique that is based on the probability idea. The classification algorithm Logistic Regression is used to predict the likelihood of a categorical dependent variable.
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Why OLS method can not be used for logistic regression?

A big reason for the omission of logistic regression (aside from the lack of time) is that it doesn't use ordinary-least-squares (OLS). With a binary outcome, OLS is not justified theoretically: errors are not normally distributed, and the variance in the errors is not constant over the range of predictor values.
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What kind of model is logistic regression?

Logistic regression is a process of modeling the probability of a discrete outcome given an input variable. The most common logistic regression models a binary outcome; something that can take two values such as true/false, yes/no, and so on.
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Can logistic regression be used for sentiment analysis?

✔️ There are 3 types of classes to be used in sentiment analysis: negative, neutral and positive. The key-value values in the Dataframe, for which the target property is specified, as 0, 2 and 4 tags below, are reduced to two in logistic regression.
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Why is logistic regression good for NLP?

Logistic regression is useful for this as it uses a sigmoid function to output a probability between zero and one. Recall that in supervised machine learning we have input features X and a set of labels Y . In order to make predictions, we need a function with parameters θ to map features to output labels ^Y .
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