Predictive Analytics Terms Glossary: Predictive Analytics Terms in 2024
A
A/B Testing
A/B Testing is an experimental method used to compare two versions (A and B) of a webpage, feature, or marketing campaign to determine which one performs better.
Anomaly Detection
Anomaly Detection is the process of identifying data points or patterns that deviate significantly from the expected or normal behavior.
Anova
Anova (Analysis of Variance) is a statistical technique used to determine if there are any statistically significant differences between the means of two or more groups.
Area Under The Curve
The Area Under the Curve (AUC) is a metric used to evaluate the performance of a binary classification model, representing the probability that the model ranks a randomly chosen positive instance higher than a randomly chosen negative instance.
Artificial Neural Network
Artificial Neural Network (ANN) is a computational model inspired by the biological neural networks in the human brain, used for various machine learning tasks.
Association Rule Learning
Association Rule Learning is a data mining technique that discovers interesting relationships or associations between variables in large datasets.
Association Rule Mining
Association Rule Mining is the process of discovering interesting associations or relationships among items in large datasets.
Auc-Roc
AUC-ROC (Area Under the ROC Curve) is a performance metric that measures the overall quality of a binary classification model by calculating the area under the ROC curve.
Automated Machine Learning
Automated Machine Learning (AutoML) is the process of automating various stages of the machine learning workflow, such as feature engineering, model selection, and hyperparameter tuning.
B
Bias
Bias is the systematic deviation of the predicted values from the true values, indicating a model's tendency to consistently overestimate or underestimate.
Bias-Variance Tradeoff
Bias-Variance Tradeoff is the balance between underfitting (high bias) and overfitting (high variance) in a predictive model, aiming to minimize the overall error.
Big Data
Big Data refers to extremely large and complex datasets that cannot be easily managed, processed, or analyzed using traditional data processing techniques.
Blockchain
Blockchain is a decentralized and distributed digital ledger technology that securely records and verifies transactions across multiple computers or nodes.
Business Intelligence
Business Intelligence (BI) refers to technologies, applications, and practices that transform raw data into meaningful and actionable insights for business decision-making.
C
Categorical Variable
A Categorical Variable is a variable that can take on one of a limited number of categories or levels.
Churn Prediction
Churn Prediction is the task of identifying customers who are likely to discontinue using a product or service based on their behavior, enabling proactive retention strategies.
Classification
Classification is a machine learning technique that predicts the class or category of a given input based on its features or characteristics.
Classification Model
A Classification Model is a statistical model used to predict the class or category of a given observation.
Cloud Computing
Cloud Computing is a model for delivering computing services over the internet, providing on-demand access to a shared pool of resources, such as servers, storage, and applications.
Clustering
Clustering is a technique used to group similar data points or objects together in order to discover underlying patterns or structures.
Coefficient
In statistics, a Coefficient represents the degree of association between two variables or the amount of change in the dependent variable for a unit change in the independent variable.
Confusion Matrix
Confusion Matrix is a table that summarizes the performance of a classification model by showing the predicted and actual class labels for a set of test data.
Correlation
Correlation measures the strength and direction of the relationship between two or more variables, indicating how changes in one variable are associated with changes in another variable.
Cross-Validation
Cross-Validation is a technique used to assess the performance and generalization ability of a predictive model by splitting the data into multiple subsets for training and testing.
Customer Segmentation
Customer Segmentation is the process of dividing customers into groups based on their characteristics, behaviors, or purchasing patterns, allowing targeted marketing and personalized services.
D
Data Governance
Data Governance is a framework that ensures data quality, security, privacy, and compliance within an organization, establishing policies and procedures for data management.
Data Mining
Data Mining is the process of discovering patterns and extracting useful information from large datasets.
Data Preprocessing
Data Preprocessing is the process of cleaning, transforming, and organizing raw data to make it suitable for further analysis.
Data Quality
Data Quality refers to the accuracy, completeness, consistency, and reliability of data, ensuring that data is fit for its intended purpose.
Data Visualization
Data Visualization is the process of representing data and information in a visual form, aiming to facilitate exploration, understanding, and communication of insights.
Data Warehouse
Data Warehouse is a centralized repository that integrates data from various sources for reporting, analysis, and decision-making purposes.
Data Wrangling
Data Wrangling, also known as Data Munging, is the process of cleaning, transforming, and mapping raw data into a usable format for analysis.
Data-Driven Decision Making
Data-driven Decision Making is an approach that relies on the analysis of data and quantitative methods to guide business decisions and strategy.
Decision Tree
Decision Tree is a simple yet powerful predictive modeling technique that uses a tree-like structure to make decisions or predictions based on a series of rules.
Deep Learning
Deep Learning is a subset of machine learning that focuses on training artificial neural networks with multiple layers to learn hierarchical representations of data.
Deep Reinforcement Learning
Deep Reinforcement Learning is a combination of deep learning and reinforcement learning, where an agent learns to take actions in an environment to maximize a reward signal.
Dimensionality Reduction
Dimensionality Reduction is the process of reducing the number of variables or features in a dataset while preserving its information content and reducing its complexity.
E
Elastic Net
Elastic Net is a technique that combines both Ridge and Lasso Regression by adding a linear combination of L1 and L2 penalties to the objective function.
Ensemble Learning
Ensemble Learning is a machine learning technique that combines multiple models or learning algorithms to improve the overall predictive accuracy.
Etl
ETL (Extract, Transform, Load) is the process of extracting data from various sources, transforming it into a consistent format, and loading it into a data warehouse or other target system.
Explainable Ai
Explainable AI is an approach that focuses on developing AI models and algorithms that can provide transparent explanations for their predictions or decisions.
F
F1 Score
F1 Score is the harmonic mean of precision and recall, providing a single metric that balances both metrics and is useful for comparing models.
Feature Engineering
Feature Engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of a machine learning algorithm.
Feature Importance
Feature Importance is a measure of the usefulness or significance of each feature in a predictive model for making accurate predictions.
Feature Scaling
Feature Scaling is the process of standardizing or normalizing the range of features in a dataset to ensure they have a similar scale.
Feature Selection
Feature Selection is the process of selecting the most relevant and informative features from a dataset for use in predictive modeling.
G
Gradient Boosting
Gradient Boosting is an ensemble learning technique that combines weak prediction models, typically decision trees, in a sequential manner to create a strong predictive model.
H
Hypothesis Testing
Hypothesis Testing is a statistical technique used to make inferences or conclusions about a population based on a sample, testing the validity of a hypothesis.
I
Imputation
Imputation is the process of filling in missing values in a dataset with estimated or imputed values.
Internet Of Things
Internet of Things (IoT) refers to the network of physical objects or devices embedded with sensors, software, and connectivity to exchange data and communicate with each other.
K
K-Means Clustering
K-means Clustering is a popular unsupervised learning algorithm used to divide a dataset into clusters based on similarities in the feature space.
K-Nearest Neighbors
K-nearest Neighbors (KNN) is a non-parametric machine learning algorithm that predicts the class label of a given input by considering the classes of its k nearest neighbors.
L
Lasso Regression
Lasso Regression is a technique used to perform variable selection and shrink the coefficients of less important variables towards zero, leading to sparse models.
Lift Chart
A Lift Chart is a graphical representation of the performance of a predictive model by comparing the cumulative response generated by the model with a random selection.
Logistic Regression
Logistic Regression is a statistical regression technique used for binary classification problems, modeling the probability of the dependent variable belonging to a certain class.
Long Short-Term Memory
Long Short-Term Memory (LSTM) is a type of recurrent neural network architecture that addresses the vanishing gradient problem and can remember long-term dependencies.
M
Machine Learning
Machine Learning is a subset of AI that focuses on getting machines to learn from data and make predictions or decisions without being explicitly programmed.
Mean Absolute Error
Mean Absolute Error (MAE) is a loss function that measures the average absolute difference between the predicted and actual values in regression models.
Mean Squared Error
Mean Squared Error (MSE) is a commonly used loss function that measures the average squared difference between the predicted and actual values in regression models.
N
Naive Bayes
Naive Bayes is a simple probabilistic classifier based on Bayes' theorem and assumes that the features are conditionally independent given the class label.
Natural Language Processing
Natural Language Processing (NLP) is a subfield of AI that focuses on the interaction between computers and humans, enabling machines to understand, interpret, and generate human language.
Neural Network
Neural Network is a type of machine learning algorithm that is inspired by the structure and functions of the human brain, consisting of interconnected layers of artificial neurons.
Numeric Variable
A Numeric Variable is a variable that represents quantities or numbers and can be measured on a numerical scale.
O
Optimization
Optimization involves finding the best solution to a problem given certain constraints or objectives, often used in predictive analytics to optimize model parameters or decision-making processes.
Outlier Detection
Outlier Detection is the process of identifying and treating data points that deviate significantly from the norm or are erroneous.
Overfitting
Overfitting occurs when a predictive model is overly complex and captures noise or random fluctuations in the training data, leading to poor performance on new, unseen data.
P
P-Value
P-value is a statistical measure that represents the probability of obtaining results as extreme as the observed results, assuming the null hypothesis is true.
Power Analysis
Power Analysis is a statistical technique used to determine the sample size needed to detect a certain effect size with a desired level of statistical power.
Precision
Precision is a performance metric that measures the proportion of true positive predictions out of the total predicted positives, indicating the model's ability to avoid false positives.
Predictive Analytics
Predictive Analytics is the use of historical data, statistical algorithms, and machine learning techniques to predict future outcomes or trends.
Principal Component Analysis
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a set of correlated variables into a new set of uncorrelated variables called principal components.
Python
Python is a popular programming language widely used in data analysis, machine learning, and scientific computing due to its simplicity and extensive libraries.
R
R
R is a programming language and environment commonly used in statistical analysis, data visualization, and machine learning, known for its vast collection of packages.
R-Squared
R-squared is a statistical metric that represents the proportion of the variance in the dependent variable that is predictable from the independent variables.
Random Forest
Random Forest is an ensemble learning algorithm that constructs a multitude of decision trees and combines their predictions to make accurate predictions.
Recall
Recall is a performance metric that measures the proportion of true positive predictions out of the actual positive instances, indicating the model's ability to avoid false negatives.
Recommendation Systems
Recommendation Systems are algorithms that provide personalized suggestions or recommendations to users based on their preferences, behaviors, or similarities to other users.
Recurrent Neural Network
Recurrent Neural Network (RNN) is a type of neural network that is capable of processing sequential data by using information from previous time steps.
Regression Analysis
Regression Analysis is a statistical technique used to model and analyze the relationships between a dependent variable and one or more independent variables.
Regression Model
A Regression Model is a statistical model used to predict a continuous target variable.
Reinforcement Learning
Reinforcement Learning is a type of machine learning where an agent learns to make decisions in an environment to maximize a reward signal.
Residuals
Residuals are the differences between the predicted values and the actual values in regression models, representing the unexplained variation in the data.
Ridge Regression
Ridge Regression is a technique used to mitigate the problem of multicollinearity in regression models by adding a penalty term to the least squares objective function.
Roc Curve
ROC Curve is a graphical representation of the trade-off between the true positive rate (sensitivity) and false positive rate (1 - specificity) for different classification thresholds.
Root Mean Square Error
Root Mean Square Error (RMSE) is a commonly used metric to measure the accuracy of a prediction model by calculating the square root of the average of the squared differences between predicted and actual values.
Root Mean Squared Error
Root Mean Squared Error (RMSE) is the square root of the mean squared error and provides a more interpretable metric for regression models.
S
Sampling
Sampling is the process of selecting a subset of individuals or observations from a larger population to infer or generalize about the entire population.
Sentiment Analysis
Sentiment Analysis is a technique that uses natural language processing and machine learning to determine the sentiment or emotion expressed in a piece of text.
Singular Value Decomposition
Singular Value Decomposition (SVD) is a matrix factorization technique that represents a matrix as the product of three matrices, enabling dimensionality reduction and data compression.
Sparse Data
Sparse Data refers to datasets in which most of the values are missing or zero, requiring specialized techniques for analysis and prediction.
Sql
SQL (Structured Query Language) is a standard programming language used to manage and analyze relational databases, enabling data retrieval, manipulation, and querying.
Statistical Modeling
Statistical Modeling is the process of estimating and making inferences about the relationships between variables using statistical techniques.
Support Vector Machine
Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression tasks, aiming to find the best hyperplane that separates data points of different classes.
Support Vector Machines
Support Vector Machines (SVM) is a supervised learning algorithm used for classification and regression analysis.
T
Tableau
Tableau is a popular data visualization tool that enables users to create interactive and visually appealing dashboards, reports, and charts.
Time Series Analysis
Time Series Analysis is a statistical technique used to analyze and forecast data points collected over time.
Time Series Decomposition
Time Series Decomposition is the process of separating a time series into its underlying trend, seasonal, and residual components.
Time Series Forecasting
Time Series Forecasting is the process of predicting future values or trends of a variable based on its historical data points collected over time.
U
Underfitting
Underfitting occurs when a predictive model is too simple and fails to capture the underlying patterns or relationships in the data, resulting in low predictive accuracy.
Unstructured Data
Unstructured Data refers to data that does not have a predefined format or organization, such as text documents, social media posts, or audio recordings.
V
Variance
Variance is the variability or spread of predicted values around the mean, indicating a model's sensitivity to fluctuations in the training data.
X
Xgboost
XGBoost (Extreme Gradient Boosting) is a scalable and efficient implementation of gradient boosting that has gained popularity due to its outstanding performance in machine learning competitions.