Booz Terms

Question 1

Active Learning

Answer 1

Intelligent sample selection to improve performance of model. Samples are selected to provide the greatest information to a learning model.

Question 2

Agent Based Simulation

Answer 2

Simulates the actions and interactions of autonomous agents.

Question 3

ANOVA

Answer 3

Hypothesis testing for differences between more than two groups.

Question 4

Association Rule Mining (Apriori)

Answer 4

Data mining technique to identify the common co-occurances of items.

Question 5

Bayesian Network

Answer 5

Models conditional probabilities amongst elements, visualized as a Directed Acyclic Graph.

Question 6

Collaborative Filtering

Answer 6

Also known as ‘Recommendation,’ suggest or eliminate items from a set by comparing a history of actions against items performed by users. Finds similar items based on who used them or similar users based on the items they use.

Question 7

Coordinate Transformation

Answer 7

Provides a different perspective on data.

Question 8

Deep Learning

Answer 8

Method that learns features that leads to higher concept learning. Usually very deep neural network architectures.

Question 9

Design of Experiments

Answer 9

Applies controlled experiments to quantify effects on system output caused by changes to inputs.

Question 10

Differential Equations

Answer 10

Used to express relationships between functions and their derivatives, for example, change over time.

Question 11

Discrete Event Simulation

Answer 11

Simulates a discrete sequence of events where each event occurs at a particular instant in time. The model updates its state only at points in time when events occur.

Question 12

Discrete Wavelet Transform

Answer 12

Transforms time series data into frequency domain preserving locality information.

Question 13

Ensemble Learning

Answer 13

Learning multiple models and combining output to achieve better performance.

Question 14

Expert Systems

Answer 14

Systems that use symbolic logic to reason about facts. Emulates human reasoning.

Question 15

Exponential Smoothing

Answer 15

Used to remove artifacts expected from collection error or outliers.

Question 16

Factor Analysis

Answer 16

Describes variability among correlated variables with the goal of lowering the number of unobserved variables, namely, the factors.

Question 17

Fast Fourier Transform

Answer 17

Transforms time series from time to frequency domain efficiently. Can also be used for image improvement by spatial transforms.

Question 18

Format Conversion

Answer 18

Creates a standard representation of data regardless of source format. For example, extracting raw UTF-8 encoded text from binary file formats such as Microsoft Word or PDFs.

Question 19

Fuzzy Logic

Answer 19

Logical reasoning that allows for degrees of truth for a statement.

Question 20

Gaussian Filtering

Answer 20

Acts to remove noise or blur data.

Question 21

Generalized Linear Models

Answer 21

Expands ordinary linear regression to allow for error distribution that is not normal.

Question 22

Genetic Algorithms

Answer 22

Evolves candidate models over generations by evolutionary inspired operators of mutation and crossover of parameters.

Question 23

Grid Search

Answer 23

Systematic search across discrete parameter values for parameter exploration problems.

Question 24

Hidden Markov Models

Answer 24

Models sequential data by determining the discrete latent variables, but the observables may be continuous or discrete.

Question 25

Hierarchical Clustering

Answer 25

Connectivity based clustering approach that sequentially builds bigger (agglomerative) or smaller (divisive) clusters in the data.

Question 26

K-means and X-means Clustering

Answer 26

Centroid based clustering algorithms, where with K means the number of clusters is set and X means the number of clusters is unknown.

Question 27

Linear, Non-linear, and Integer Programming

Answer 27

Set of techniques for minimizing or maximizing a function over a constrained set of input parameters.

Question 28

Markov Chain Monte Carlo (MCMC)

Answer 28

A method of sampling typically used in Bayesian models to estimate the joint distribution of parameters given the data.

Question 29

Monte Carlo Methods

Answer 29

Set of computational techniques to generate random numbers.

Question 30

Naive Bayes

Answer 30

Predicts classes following Bayes Theorem that states the probability of an outcome given a set of features is based on the probability of features given an outcome.

Question 31

Neural Networks

Answer 31

Learns salient features in data by adjusting weights between nodes through a learning rule.

Question 32

Outlier Removal

Answer 32

Method for identifying and removing noise or artifacts from data.

Question 33

Principal Components Analysis

Answer 33

Enables dimensionality reduction by identifying highly correlated dimensions.

Question 34

Random Search

Answer 34

Randomly adjust parameters to find a better solution than currently found.

Question 35

Regression with Shrinkage (Lasso)

Answer 35

A method of variable selection and prediction combined into a possibly biased linear model.

Question 36

Sensitivity Analysis

Answer 36

Involves testing individual parameters in an analytic or model and observing the magnitude of the effect.

Question 37

Simulated Annealing

Answer 37

Named after a controlled cooling process in metallurgy, and by analogy using a changing temperature or annealing schedule to vary algorithmic convergence.

Question 38

Stepwise Regression

Answer 38

A method of variable selection and prediction. Akaike’s information criterion AIC is used as the metric for selection. The resulting predictive model is based upon ordinary least squares, or a general linear model with parameter estimation via maximum likelihood.

Question 39

Stochastic Gradient Descent

Answer 39

General-purpose optimization for learning of neural networks, support vector machines, and logistic regression models.

Question 40

Support Vector Machines

Answer 40

Projection of feature vectors using a kernel function into a space where classes are more separable.

Question 41

Term Frequency / Inverse Document Frequency

Answer 41

A statistic that measures the relative importance of a term from a corpus.

Question 42

Topic Modeling (Latent Dirichlet Allocation)

Answer 42

Identifies latent topics in text by examining word co-occurrence.

Question 43

Tree Based Methods

Answer 43

Models structured as graph trees where branches indicate decisions.

Question 44

T-Test

Answer 44

Hypothesis test used to test for differences between two groups.

Question 45

Wrapper Methods

Answer 45

Feature set reduction method that utilizes performance of a set of features on a model, as a measure of the feature set’s performance. Can help identify combinations of features in models that achieve high performance.