A linear model predicts an output as a linear combination of input variables and model parameters.
Linear regression provides a quickly implemented predictive model for establishing baseline performance before more advanced modelling work. section 6.6.2
Use the model to:
Estimate inventory demand and customer churn rates. section 6.6.2
Compare predictions with observed fluctuations to identify early signals. section 6.6.2
Record assumptions and treat promising results as provisional rather than solved. section 6.6.2
How Delivering Machine Learning Projects: From design to deployment covers it
How deep it goes
Shows a quick implementation, correlated predictions, and explicit data assumptions, but stops short of solving the modelling problem.
Examples
Uses inventory demand, customer churn rates, limited datasets, and observed fluctuations as evaluation examples.
What it assumes
Assumes an available dataset and requires careful recording of the assumptions made during rapid implementation.
What you can do after
Evaluate model feasibility by producing baseline predictions and comparing them with observed fluctuations.
How it connects
Connects exploratory data analysis, baseline evaluation, predictive analytics, and model CI/CD pipelines.
Causal Inference for Data ScienceLinear regression is a regression method that estimates a linear relationship between an outcome and one or more predictors.
Statistics Every Programmer NeedsLinear regression is a statistical modeling technique that represents the relationship between a response variable and one or more predictors with a linear function.
Deep Learning with R, Third EditionA linear transform is a mapping that preserves vector addition and scalar multiplication and can be represented by matrix multiplication.
Statistics Slam DunkA multiple linear regression model represents a target variable as a function of two or more predictor variables, typically using a linear form.
Grokking AI Algorithms, Second EditionLinear regression is a statistical modeling method that represents a continuous response as a linear function of one or more input variables.
Machine Learning System DesignA linear model predicts an output as a linear combination of input features, optionally followed by a task-specific link or threshold function.
Causal AIA linear model represents an outcome as a linear or affine function of predictor variables and model parameters, typically with an error term.
Bayesian Optimization in ActionLinear regression is a model that represents a response as a linear combination of input features and fitted coefficients.
Advanced Algorithms and Data StructuresLinear regression is a supervised-learning technique that models the relationship between input variables and a real-valued dependent variable using a parameterized function.
confounderaddresses · A confounder is a variable associated with both an explanatory or treatment variable and an outcome, whose influence can distort their apparent relationship or estimated causal effect.
inputproduces · An input is a value, information, or signal supplied to a system, computation, or interface for processing or to influence its behavior.
supervised learningkind of · Supervised learning is a machine-learning approach that learns to map inputs to target outputs from examples paired with known labels or values.
classifiercontrasts with · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.
