Logistic regression

Q: How deeply does Starting Data Analytics with Generative AI and Python cover logistic regression?

Positions logistic regression as an inferential technique for identifying predictors, without explaining estimation, assumptions, or implementation.

As explained in Starting Data Analytics with Generative AI and Python

Logistic regression is a regression method that models the probability of a categorical outcome, commonly a binary outcome.

Logistic regression models the probability of a categorical outcome, especially a binary one. It fits the stated preference for practical descriptive and inferential analysis while avoiding more advanced machine-learning methods. section 3.1 section 3.2

Use logistic regression to:

Identify predictors of an outcome such as order completion or customer satisfaction. section 3.1 section 3.2
Extend descriptive summaries and group comparisons with an inferential model. section 3.2
Support recommendations about business efficiency, while treating relationships as findings to investigate rather than automatic causes. section 3.2

E-commerce question

An analyst can examine whether delivery time, product price, payment method, or installment count helps predict order completion or customer satisfaction. A significant relationship still requires careful interpretation: a regression result can support a decision only after checking the analysis design, limitations, and business consequences. section 3.1 section 3.2

How Starting Data Analytics with Generative AI and Python covers it

Cover of Starting Data Analytics with Generative AI and Python

liveBook manning.com

Read Starting Data Analytics with Generative AI and Python on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Grokking Machine LearningA perceptron-like model that outputs a continuous value between 0 and 1 rather than a discrete class label.
liveBook manning.com

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.
liveBook manning.com

Grokking StatisticsA regression method that models the probability of a categorical outcome using a logistic link function.
liveBook manning.com

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.
liveBook manning.com

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.
liveBook manning.com

Machine Learning for Tabular Data: XGBoost, Deep Learning, and AILogistic regression is a regression model that estimates class probabilities using a logistic link function.
liveBook manning.com

Effective Data AnalysisLogistic regression is a statistical model for estimating the relationship between a categorical outcome and one or more predictors.
liveBook manning.com

Data Without LabelsA classification method that models class-event probabilities using a logistic or sigmoid transformation of input variables.
liveBook manning.com

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.
liveBook manning.com

Machine Learning Algorithms in DepthA probabilistic classification model that maps inputs to class probabilities using a logistic or softmax transformation of a linear score.
liveBook manning.com

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 Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.