Logistic regression

As explained in Effective Data Analysis

Logistic regression is a statistical model for estimating the relationship between a categorical outcome and one or more predictors.

Logistic regression is a predictive model for estimating relationships between a categorical dependent variable and one or more independent variables. section 4.2.1

Use logistic regression when:

• The outcome is categorical rather than continuous. section 4.2.1
• One or more predictors are used to estimate or predict that outcome. section 4.2.1
• Parametric assumptions and data shape are considered before making inferences. section 4.2.1

Predicting advertisement clicks

Predict whether a user will click an advertisement—yes or no—using age and the number of times the user has viewed it. section 9.1.3

Figure 4.19 Parametric statistics assume that the population data follows a specific probability distribution and that you can make inferences about the parameters of that distribution based on your sample data.

Use the figure to review the distributional assumptions underlying parametric statistical methods. section 4.2.1

How Effective Data Analysis covers it

How deep it goes

Defines the model and its role, relates it to linear regression, and defers detailed regression methods.

Examples

Uses advertisement-click prediction from user age and viewing frequency as a categorical-outcome example.

What it assumes

Builds on predictors, dependent variables, parametric statistics, and distributional assumptions.

What you can do after

Evaluate whether a categorical-outcome prediction problem calls for logistic regression rather than linear regression.

How it connects

Connects logistic regression with linear regression, parametric statistics, predictors, dependent variables, and statistical assumptions.

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Also explained in

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  Grokking Machine LearningA perceptron-like model that outputs a continuous value between 0 and 1 rather than a discrete class label.
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  Statistics Slam DunkLogistic regression is a statistical model that predicts the probability of a categorical outcome, typically by applying a logistic function to a linear predictor.
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  Grokking StatisticsA regression method that models the probability of a categorical outcome using a logistic link function.
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  Natural Language Processing in Action, Second EditionLogistic regression is a statistical classification model that estimates the probability of a class from a weighted combination of input features.
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  Causal Inference for Data ScienceLogistic regression is a statistical model that estimates the probability of a categorical, often binary, outcome using a logistic function of input variables.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AILogistic regression is a regression model that estimates class probabilities using a logistic link function.
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  Data Without LabelsA classification method that models class-event probabilities using a logistic or sigmoid transformation of input variables.
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  Ensemble Methods for Machine LearningLogistic regression is a statistical classification model that estimates class probabilities by applying a logistic function to a linear combination of input features.
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  Starting Data Analytics with Generative AI and PythonLogistic regression is a regression method that models the probability of a categorical outcome, commonly a binary outcome.
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  Machine Learning Algorithms in DepthA probabilistic classification model that maps inputs to class probabilities using a logistic or softmax transformation of a linear score.
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  The Art of AI Product DevelopmentLogistic regression is a statistical classification model that estimates class probabilities using a logistic function of input features.

Compare how 12 books explain logistic regression →

Related concepts

• maximum likelihood estimationuses · Maximum likelihood estimation is a statistical method that selects parameter values maximizing the likelihood of observed data under a specified probabilistic model.
• classifierkind of · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• sigmoiduses · A sigmoid is a smooth, bounded, S-shaped function that maps real-valued inputs between two limiting values, commonly zero and one.
• generalized linear modelkind of · A generalized linear model combines a linear predictor, a link function, and a response distribution from the exponential family.
• linear regressioncontrasts with · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.