Predictions

Also called recommendations, these are the entities a system recommends. In our case, it’s going to be the people who appear likely to give.

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Nonprofit AI: Table of Contents

Accuracy

Algorithm

Append

Area under the ROC Curve (AUC)

Artificial Intelligence (AI)

Attribute

Bias

Binary classification

Boosting

Categorical data

Class

Classification

Classification model

Classification threshold

Clustering

Confusion matrix

Cross validation

Cutoff

Data

Data aggregator

Data field

Data point

Data set

Database

Decision boundary

Decision tree

Deep learning

Dimensionality reduction

Dimensions

Enrich

Ensemble

Example

Feature

Feature engineering

Feature extraction

Feature reduction

Feature selection

Feature set

First-party data

Hold-out

Hyperparameter

Hyperparameter optimization

Instance

Items

Kernel Support Vector Machines (KSMV)

Label

Labeled record

Lead scoring

Machine Learning (ML)

Metric

Model

Negative class

Negative training data

Neural network

Observation

Overfitting

Parameter

Positive class

Positive training data

Post-processing

PR AUC (Area Under the PR Curve)

Pre-processing

Precision

Prediction

Predictive analytics

Predictive lead scoring

Production data

Proxy label

Random forest

Recall

Recommendation

Recommendation system

Record

Regression

Sample

Scoring

Stacking

Supervised learning

Synthetic

Target data

Target value

Test data

Third-party data

Training data

Unsupervised learning

Validation data

Variable

Weight

Zero party data