A-Z of Machine Learning and Computer Vision Terms

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A
A
AI Agent
AI Agent
AI Assistants
AI Assistants
AI Assisted Labeling
AI Assisted Labeling
Active Learning
Active Learning
Algorithm
Algorithm
Anchor Boxes
Anchor Boxes
Anomaly Detection
Anomaly Detection
Artificial Intelligence (AI)
Artificial Intelligence (AI)
Attribute
Attribute
B
B
Backpropagation
Backpropagation
Bagging
Bagging
Batch
Batch
Batch Normalization
Batch Normalization
Bayesian Network
Bayesian Network
Bias
Bias
Big Data
Big Data
Binary Classification
Binary Classification
Blur
Blur
Boosting
Boosting
Bounding Box
Bounding Box
C
C
COCO
COCO
Calibration
Calibration
Calibration Curve
Calibration Curve
Canonical Correlation Analysis (CCA)
Canonical Correlation Analysis (CCA)
Case-Based Reasoning
Case-Based Reasoning
Chain of Thought (CoT)
Chain of Thought (CoT)
ChatGPT
ChatGPT
Chi-Squared Automatic Interaction Detection (CHAID)
Chi-Squared Automatic Interaction Detection (CHAID)
Class Boundary
Class Boundary
Class Boundary (Statistics & Machine Learning)
Class Boundary (Statistics & Machine Learning)
Class Imbalance
Class Imbalance
Clustering
Clustering
Collaborative Filtering
Collaborative Filtering
Computer Vision
Computer Vision
Computer Vision Model
Computer Vision Model
Concept Drift
Concept Drift
Conditional Random Field (CRF)
Conditional Random Field (CRF)
Confusion Matrix
Confusion Matrix
Constrained Clustering
Constrained Clustering
Contrastive Learning
Contrastive Learning
Convolutional Neural Network (CNN)
Convolutional Neural Network (CNN)
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs)
Cross-Validation
Cross-Validation
D
D
DICOM
DICOM
Data Approximation
Data Approximation
Data Augmentation
Data Augmentation
Data Drift
Data Drift
Data Error
Data Error
Data Mining
Data Mining
Data Operations
Data Operations
Data Pre-processing
Data Pre-processing
Data Quality
Data Quality
Dataset
Dataset
Decision Boundary
Decision Boundary
Decision List
Decision List
Decision Stump
Decision Stump
Decision Tree
Decision Tree
Deep Learning
Deep Learning
Deep Neural Networks
Deep Neural Networks
Dimensionality Reduction
Dimensionality Reduction
Dropout
Dropout
Dynamic and Event-Based Classifications
Dynamic and Event-Based Classifications
E
E
Edge Cases
Edge Cases
Edge Computing
Edge Computing
Edge Detection
Edge Detection
Elastic Net
Elastic Net
Embedding Spaces
Embedding Spaces
Ensemble Learning
Ensemble Learning
Epoch
Epoch
Expectation-Maximization Algorithm (EM)
Expectation-Maximization Algorithm (EM)
Extreme Learning Machine
Extreme Learning Machine
F
F
F1 Score
F1 Score
FP-Growth Algorithm
FP-Growth Algorithm
Factor Analysis
Factor Analysis
False Positive Rate
False Positive Rate
Feature
Feature
Feature Engineering
Feature Engineering
Feature Extraction
Feature Extraction
Feature Hashing
Feature Hashing
Feature Learning
Feature Learning
Feature Scaling
Feature Scaling
Feature Selection
Feature Selection
Feature Vector
Feature Vector
Few-shot Learning
Few-shot Learning
Fisher’s Linear Discriminant
Fisher’s Linear Discriminant
Foundation Models
Foundation Models
Frame Rate
Frame Rate
Frames Per Second (FPS)
Frames Per Second (FPS)
Fully Connected Layer
Fully Connected Layer
Fuzzy Logic
Fuzzy Logic
G
G
Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)
Generative Adversarial Networks
Generative Adversarial Networks
Generative Pre-Trained Transformer
Generative Pre-Trained Transformer
C

Chi-Squared Automatic Interaction Detection (CHAID)

Chi-squared Automatic Interaction Detection (CHAID) is a decision tree learning algorithm that uses chi-square statistical tests to determine how to split data at each step​.It is one of the earliest decision tree methods (developed by Gordon V. Kass in 1980) and is designed to handle categorical predictors by finding statistically significant splits without requiring binary partitions. At each node of the tree, CHAID examines all possible splits of the input features and performs a chi-square independence test between each feature and the target outcome to evaluate how significant the association is​. The algorithm may merge categories of a predictor that are not significantly different with respect to the target (using methods like Bonferroni or Holm corrections to adjust for multiple comparisons) and chooses the split that yields the most significant p-value​.This often results in multi-way splits: a single categorical feature might split into more than two branches if multiple distinct groups of categories are found, as opposed to binary splits in CART. The tree growing continues recursively – performing chi-square tests at each node – until no further statistically significant splits can be made (based on a chosen significance threshold) or until other stopping criteria (like minimum node size) are reached​.The output of CHAID is a decision tree where each internal node is an attribute and its branches correspond to groups of attribute values that differentiated the outcomes. Because CHAID uses a significance test, it naturally handles interaction detection – it finds interactions between predictors in terms of how they affect the response (hence the name). It has some advantages: since it can produce multi-branch splits, the resulting tree can be more compact and interpretable in certain cases, and it doesn’t assume monotonic relationships or require binary encoding of categorical variables. However, multi-way splits mean that CHAID typically needs a large sample size to ensure that each child node has enough data; otherwise, the chi-square tests might not detect significance reliably​.CHAID is non-parametric and does not assume linear relationships, making it flexible for exploration. It’s been widely used in marketing analytics (for customer segmentation based on survey responses, for example) and in other social science and medical domains where researchers want an explainable tree that highlights which input variables (and value groupings) lead to different outcomes. The reliance on chi-square tests means it works best when the target is categorical (for prediction/classification tasks) or can be discretized into categories (for, say, ranking or segmentation tasks). Overall, CHAID is a useful tool when one’s goal is to uncover statistically significant splits and interactions in data and produce an easily interpretable decision tree.

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