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Predictive Model
A mathematical algorithm that predicts a target variable from explanatory variables.
Classification
The process of using predictor information to sort data samples into distinct classes.
Numeric Prediction
Predicting the numerical value of a dependent variable using independent variables.
Rule Induction
A process of deducing if-then rules from a data set.
Decision Tree
A model that splits data on every node leading to a leaf where the class is identified.
K-Nearest Neighbors (k-NN)
A method that classifies an unknown record based on its nearest neighbors in the training data.
Eager Learners
Models that develop a mathematical relationship between input and target variables.
Lazy Learners
Models that use a lookup table to match input variables with outcomes.
Overfitting
The tendency to tailor models too closely to training data, impacting generalization.
Pruning
The process of reducing the size of a decision tree to prevent overfitting.
Cross-Validation
A training and testing procedure that provides estimates of model generalization performance.
Confusion Matrix
A matrix that counts correct and false classifications to assess predictive capability.
Gains & Lift Charts
Charts that measure the effectiveness of a classification model against a baseline.
Test Set Validation
Holding out data to assess how well a model generalizes to unseen cases.
Generalization Performance
The ability of a model to apply to unseen data that was not part of the training set.
Domain Knowledge Validation
Sanity checking a model by getting assessments from domain experts.
Majority Vote
A method in k-NN where the class label is determined by the most common label among the nearest neighbors.
Distance Measure
A method for computing the distance between different records in k-NN.
Model Complexity
The intricacy of a model, which can lead to overfitting if not managed appropriately.
Attribute (in classification context)
Features or characteristics used to sort data samples into classes.
