Glossary of Terms
This glossary defines key terms used throughout Atlas documentation.
A
- AbstractModel
Base class that all models must inherit from. Defines the interface for model integration including
predict()andcontributions()methods.- Acquisition Function
In Bayesian optimization, a function that determines which point to evaluate next based on the posterior distribution. Common types include Expected Improvement (EI) and Upper Confidence Bound (UCB).
- Adstock
See Carryover Effect.
- Allocation
The distribution of budget or resources across different channels, time periods, or categories.
- API (Application Programming Interface)
A set of protocols and tools for building software applications. The framework provides REST APIs for model serving and optimization.
B
- Baseline
The reference point for comparison, typically the current or historical budget allocation used to measure improvement.
- Bayesian Optimization
An optimization technique that builds a probabilistic model of the objective function and uses it to select the most promising points to evaluate.
- Black-box Model
A model where the internal workings are unknown or inaccessible, only input-output relationships are observable.
- Budget
The total amount of resources (usually monetary) available for allocation across different channels or categories.
- Business Constraint
Limitations imposed by business rules, policies, or practical considerations (e.g., minimum spend requirements, market share limits).
C
- Carryover Effect
The phenomenon where marketing impact extends beyond the initial period of spend, also known as adstock.
- Channel
A distinct marketing or distribution method (e.g., TV, digital, radio) that can receive budget allocation.
- Constraint
A limitation or restriction on the optimization problem, such as budget limits, bounds, or business rules.
- Contribution Analysis
The process of determining how much each input variable contributes to the model’s output prediction.
- Convergence
The state when an optimization algorithm has found a solution that satisfies stopping criteria and further iterations don’t significantly improve the result.
- Convex Optimization
A class of optimization problems where the objective function is convex and the feasible region is a convex set, guaranteeing a global optimum.
- CVXPY
A Python library for convex optimization that allows natural mathematical notation for problem specification.
D
- Data Pipeline
The series of data processing steps from raw input to model-ready format.
- Diminishing Returns
The principle that each additional unit of input produces progressively smaller increases in output.
- Docker
A platform for developing, shipping, and running applications in containers, used for model isolation and deployment.
- Drift
The phenomenon where model performance degrades over time due to changes in the underlying data distribution.
E
- Efficient Frontier
See Pareto Frontier.
- Elasticity
The percentage change in output (e.g., sales) resulting from a percentage change in input (e.g., marketing spend).
- Ensemble Model
A model that combines predictions from multiple individual models to improve overall performance.
- Epoch
One complete pass through the entire dataset during model training.
F
- Feasible Region
The set of all possible solutions that satisfy all constraints.
- Feature
An individual measurable property or characteristic used as input to a model.
- Frequency Capping
Limiting the number of times an individual is exposed to an advertisement within a specific time period.
G
- Global Optimum
The best possible solution across the entire search space, as opposed to a local optimum.
- Gradient
The vector of partial derivatives of a function, indicating the direction of steepest increase.
- Grid Search
An exhaustive search method that evaluates all combinations of a discrete set of parameter values.
H
- Heuristic
A practical problem-solving approach that uses shortcuts to produce good-enough solutions quickly.
- Hill Transformation
A mathematical function used to model saturation effects in marketing response curves.
- Hyperparameter
A parameter whose value is set before the learning process begins, controlling the learning process itself.
I
- Incrementality
The additional impact generated by a marketing activity beyond what would have occurred naturally.
- Integer Programming
Optimization where some or all variables are restricted to integer values.
- Iteration
A single step in an optimization algorithm where the solution is updated.
J
- JSON (JavaScript Object Notation)
A lightweight data interchange format used for configuration and API communication.
K
- KPI (Key Performance Indicator)
A measurable value that demonstrates how effectively objectives are being achieved (e.g., revenue, conversions, awareness).
L
- Lever
An optimization variable that can be adjusted to influence outcomes (e.g., budget allocation to a channel).
- Linear Programming
A method for optimizing a linear objective function subject to linear constraints.
- Local Optimum
A solution that is optimal within a neighboring set of solutions but may not be the global optimum.
- Loss Function
A function that measures the difference between predicted and actual values, which optimization seeks to minimize.
M
- Marginal ROI
The return on investment for the next incremental dollar spent.
- Marketing Mix Modeling (MMM)
Statistical analysis technique that measures the impact of marketing activities on sales.
- Metadata
Data that provides information about other data, such as model version, training date, or performance metrics.
- Multi-objective Optimization
Optimization involving multiple, often conflicting objectives that must be balanced.
N
- Non-convex Optimization
Optimization problems where the objective function or constraints are non-convex, potentially having multiple local optima.
- Normalization
The process of scaling data to a standard range, typically [0, 1] or [-1, 1].
O
- Objective Function
The function being optimized (maximized or minimized) in an optimization problem.
- Optuna
A hyperparameter optimization framework that uses advanced sampling algorithms for black-box optimization.
- Overfitting
When a model learns the training data too well, including noise, reducing its ability to generalize.
P
- Pareto Frontier
The set of solutions where no objective can be improved without degrading another objective.
- Pareto Optimal
A solution is Pareto optimal if no other solution improves at least one objective without worsening another.
- Pipeline
A sequence of data processing steps, from raw input to final output.
- Prediction Interval
A range of values within which future observations are expected to fall with a certain probability.
Q
- Quadratic Programming
Optimization of a quadratic objective function subject to linear constraints.
R
- Reach
The number of unique individuals exposed to a marketing message.
- Regularization
Technique to prevent overfitting by adding a penalty term to the loss function.
- Response Curve
The relationship between marketing input (e.g., spend) and output (e.g., sales).
- ROI (Return on Investment)
The ratio of net profit to the cost of investment.
- ROAS (Return on Ad Spend)
Total revenue generated per dollar spent on advertising.
S
- Saturation
The point at which additional marketing spend yields diminishing or no returns.
- Scenario Analysis
Evaluating different possible future situations and their potential impacts.
- SciPy
Scientific computing library for Python that includes optimization algorithms.
- Sensitivity Analysis
The study of how changes in input variables affect the output.
- Simplex Algorithm
An algorithm for solving linear programming problems.
- Stochastic Optimization
Optimization methods that incorporate randomness, useful for problems with uncertainty.
- Surrogate Model
A simplified approximation of a complex model used to speed up optimization.
T
- Time Series
A sequence of data points indexed in time order.
- TPE (Tree-structured Parzen Estimator)
A Bayesian optimization algorithm used in Optuna for hyperparameter tuning.
- Trade-off
A situation where improving one objective requires degrading another.
U
- Uncertainty Quantification
The process of quantifying and characterizing uncertainty in model predictions.
- Underfitting
When a model is too simple to capture the underlying patterns in the data.
V
- Validation
The process of assessing whether a model’s predictions are accurate on unseen data.
- Variable Bounds
Minimum and maximum allowed values for optimization variables.
- Vectorization
The process of converting operations to work on entire arrays rather than individual elements.
W
- Warm Start
Initializing an optimization algorithm with a good starting point based on prior knowledge.
- Weight
The relative importance assigned to different objectives in multi-objective optimization.
- What-if Analysis
Exploring how changes in inputs affect outputs without actually implementing the changes.
X
- Xarray
Python library for working with labeled multi-dimensional arrays, used for data management in the framework.
Y
- Yield
The return or output generated from a given input or investment.
Z
- Zero-sum
A situation where total gains equal total losses, common in competitive market share scenarios.
Technical Acronyms
API: Application Programming Interface
CI/CD: Continuous Integration/Continuous Deployment
CPU: Central Processing Unit
CSV: Comma-Separated Values
GPU: Graphics Processing Unit
HTTP: Hypertext Transfer Protocol
JSON: JavaScript Object Notation
KPI: Key Performance Indicator
ML: Machine Learning
MMM: Marketing Mix Modeling
REST: Representational State Transfer
ROI: Return on Investment
ROAS: Return on Ad Spend
SDK: Software Development Kit
URL: Uniform Resource Locator
UUID: Universally Unique Identifier
YAML: Yet Another Markup Language
Mathematical Notation
∇: Gradient (nabla)
∂: Partial derivative
Σ: Summation
∏: Product
∈: Element of
⊆: Subset of
∀: For all
∃: There exists
arg max: Argument of the maximum
arg min: Argument of the minimum
s.t.: Subject to (in optimization)
w.r.t.: With respect to
This glossary is a living document. If you encounter terms that should be added or clarified, please contribute to the documentation.