Data Mining - User Group
Table of Contents
Articles Related
Data Mining Group (DMG)
The Data Mining Group (DMG) is an independent, vendor led consortium that develops data mining standards, such as the Predictive Model Markup Language (PMML).
- The 1 Percent Rule
- (Absolute|True) Zero
- (Parameters | Model) (Accuracy | Precision | Fit | Performance) Metrics
- Adjusted R^2
- Akaike information criterion (AIC)
- Algorithms
- (Anomaly|outlier) Detection
- Apriori algorithm
- Association (Rules Function|Model) - Market Basket Analysis
- Attribute (Importance|Selection) - Affinity Analysis
- Area under the curve (AUC)
- Automatic Discovery
- Bootstrap aggregating (bagging)
- (Base rate fallacy|Bonferroni's principle)
- (Baseline|Naive) classification (Zero R)
- Bayes’ Theorem (Probability)
- Bayesian
- Benford's law (frequency distribution of digits)
- Best Subset Selection Regression
- Bias (Sampling error)
- Bias-variance trade-off (between overfitting and underfitting)
- Bayesian Information Criterion (BIC)
- R (Big R)
- Bimodal Distribution
- Binary logistic regression
- Mathematics - Combination (Binomial coefficient|n choose k)
- (Probability|Statistics) - Binomial Distribution
- Data Mining, Book
- (Boosting|Gradient Boosting|Boosting trees)
- Decision boundary Visualization
- (C4.5|J48) algorithm
- Documentation / Reference
- Causation - Causality (Cause and Effect) Relationship
- Cumulative Distribution Function (CDF)
- Centering Continous Predictors
- Central limit theorem (CLT)
- centroid (center of gravity)
- Chance
- Data-Science - Cheatsheet
- (Class|Category|Label) Target
- (Classifier|Classification Function)
- Clustering (Function|Model)
- Coin Flipping
- (Prediction|Recommender System) - Collaborative filtering
- Competitions (Kaggle and others)
- Statistics - (Confidence|likelihood) (Prediction probabilities|Probability classification)
- Confidence Interval
- Confounding (factor|variable) - (Confound|Confounder)
- Confusion Matrix
- Content Analysis and Acquisition
- Continuous Variable
- Convex
- Correlation (Coefficient analysis)
- Cosine Similarity (Measure of Angle)
- Covariance
- Mallow's Cp
- Cross Product (of X and Y) (CP|SP)
- (Statistics|Data Mining) - (K-Fold) Cross-validation (rotation estimation)
- (Periodicity|Periodic phenomena|Cycle)
- (Data|Knowledge) Discovery - Statistical Learning
- Data Point
- Data (Preparation | Wrangling | Munging)
- Data Product
- Data - Science
- Data Scientist
- Decision Tree (DT) Algorithm
- Decision Stump
- Deep Learning (Network)
- (Degree|Level) of confidence
- Degree of freedom (df)
- (dependent|paired sample) t-test
- Math - Derivative (Sensitivity to Change, Differentiation)
- Design Matrix (X)
- Deviance
- Deviation Score (for one observation)
- Rolling a die (many dice)
- Dimensionality (number of variable, parameter) (P)
- (Dimension|Feature) (Reduction)
- (Data|Text) Mining - Word-sense disambiguation (WSD)
- (Discretizing|binning) (bin)
- Discriminant analysis
- Quadratic discriminant analysis (QDA)
- (Discriminative|conditional) models
- Distance
- Statistics / Distribution - (Function)
- Dummy (Coding|Variable) - One-hot-encoding (OHE)
- Effects (between predictor variable)
- Effect Size
- Elastic Net Model
- Ensemble Learning (meta set)
- Entropy (Information Gain)
- Prediction Error (Training versus Test)
- (Error|misclassification) Rate - false (positives|negatives)
- (Estimation|Approximation)
- (Estimator|Point Estimate) - Predicted (Score|Target|Outcome| )
- Exponential Distribution
- F-distributions
- (F-Statistic|F-test|F-ratio)
- Face Recognition
- (Factor Variable|Qualitative Predictor)
- Factor Analysis
- Factorial Anova
- Feature Engineering
- (Feature|Attribute) Extraction Function
- Feature Hashing
- (Attribute|Feature) (Selection|Importance)
- Fraud Detection
- (Frequency|Rate)
- Frequency Distribution
- (Frequent itemsets|co-occurring items)
- Frequentist
- Data Model - Fudge factor
- Fuzzy Logic (Partial Truth)
- Galton board
- Generalized additive model (GAM)
- Statistics / Gaussian function ( )
- Gaussian processes (modelling probability distributions over functions)
- Generalized Boosted Regression Models
- Generative Model
- Getting Started
- Generalized Linear Models (GLM) - Extensions of the Linear Model
- (Stochastic) Gradient descent (SGD)
- User Group
- Grouping (Classification)
- Head
- Hierarchical Clustering
- Hierarchy
- High Dimension (Curse of Dimensionality)
- Data Science - History
- Homoscedasticity
- Hypothesis (Tests|Testing)
- ID3 Algorithm
- Intrusion detection systems (IDS) / Intrusion Prevention / Misuse
- Image classification
- independent t-test
- Statistical - Inference
- Information Gain
- Information Retrieval
- (Interaction|Synergy) effect
- Intercept - Regression (coefficient|constant)
- Model Interpretation
- (Interval|Delta) (Measurement)
- Java API for data mining (JDM)
- k-Means Clustering algorithm
- Kernel
- K-Nearest Neighbors (KNN) algorithm - Instance based learning
- Knots (Cut points)
- Kurtosis (Distribution Tail extremity)
- Statistical Learning - Lasso
- Standard Least Squares Fit (Gaussian linear model)
- Leptokurtic distribution
- (Level|Label)
- (Lying|Lie)
- (Life cycle|Project|Data Pipeline)
- Lift Chart
- Statistical Learning - Simple Linear Discriminant Analysis (LDA)
- Fisher (Multiple Linear Discriminant Analysis|multi-variant Gaussian)
- Linear (Regression|Model)
- (Linear spline|Piecewise linear function)
- Little r - (Pearson product-moment Correlation coefficient)
- Global vs Local
- LOcal (Weighted) regrESSion (LOESS|LOWESS)
- log-likelihood function (cross-entropy)
- Logistic regression (Classification Algorithm)
- (Logit|Logistic) (Function|Transformation)
- Loss functions (Incorrect predictions penalty)
- Data Science - (Kalman Filtering|Linear quadratic estimation (LQE))
- Machine Learning
- Main Effect
- Probability mass function (PMF)
- Function - Maximum (Max)
- Maximum Entropy Algorithm
- Maximum likelihood
- Measure
- Measurement
- (Missing Value|Not Available) NA
- Model Size (d)
- Model vs Expert
- Moderator Variable (Z) - Moderation
- (Average|Mean) Squared (MS) prediction error (MSE)
- Multi-variant logistic regression
- Multi-class (classification|problem)
- (Multiclass Logistic|multinomial) Regression
- Multidimensional scaling ( similarity of individual cases in a dataset)
- Multiple Linear Regression
- Naive Bayes (NB)
- (Probabilistic?) Neural Network (PNN)
- (No Predictor|Mean|Null) Model
- Noise (Unwanted variation)
- Non-linear (effect|function|model)
- Non-Negative Matrix Factorization (NMF) Algorithm
- Multi-response linear regression (Linear Decision trees)
- (Normal|Gaussian) Distribution - Bell Curve
- Orthogonal Partitioning Clustering (O-Cluster or OC) algorithm
- Odds (Ratio)
- (One|Simple) Rule - (One Level Decision Tree)
- Outliers Cases
- (Overfitting|Overtraining|Robust|Generalization) (Underfitting)
- Data Science - Over-generalization
- (Paretian|Power law) distribution
- Pareto ( Principle | Distribution )
- Pascal Triangle
- What is a Pattern ?
- Principal Component (Analysis|Regression) (PCA|PCR)
- (Probability) Density Function (PDF)
- Mathematics - Permutation (Ordered Combination)
- Piecewise polynomials
- Partial least squares (PLS)
- Predictive Model Markup Language (PMML)
- Poisson Distribution
- (Global) Polynomial Regression (Degree)
- Population Parameter
- Post-hoc test
- Power of a test
- (Prediction|Guess)
- Predictive Model Markup Language (PMML)
- (Machine|Statistical) Learning - (Predictor|Feature|Regressor|Characteristic) - (Independent|Explanatory) Variable (X)
- Probability (of an event) / Likelihood
- Probit Regression (probability on binary problem)
- Problem
- Process control (SPC)
- Pruning (a decision tree, decision rules)
- Pytorch
- R-squared ( |Coefficient of determination) for Model Accuracy
- Random forest
- Random Variable (Random quantity|Aleatory variable|Stochastic variable)
- Range
- Rare Event
- (Fraction|Ratio|Percentage|Share) (Variable|Measurement)
- Raw score
- recommendations
- Regression
- (Regression Coefficient|Weight|Slope) (B)
- Assumptions underlying correlation and regression analysis (Never trust summary statistics alone)
- (Machine learning|Inverse problems) - Regularization
- Reinforcement learning
- Sampling - Sampling (With|without) replacement (WR|WOR)
- Research
- (Residual|Error Term|Prediction error|Deviation) (e| )
- Resistant
- Result Considerations
- Ridge regression
- Root Mean Square (RMS)
- Root mean squared (Error|Deviation) (RMSE|RMSD)
- ROC Plot and Area under the curve (AUC)
- Rote Classifier
- Residual sum of Squares (RSS) = Squared loss ?
- (Decision) Rule
- Sampling
- Sampling Distribution
- Sampling Error
- Scale
- (Scales of measurement|Type of variables)
- Scoring (Applying)
- (Shrinkage|Regularization) of Regression Coefficients
- Signal (Wanted Variation)
- Significance level
- (Significance | Significant) Effect
- Similarity
- Simple Effect
- (Univariate|Simple) Logistic regression
- (Univariate|Simple|Basic) Linear Regression
- Skew (-ed Distribution|Variable)
- Software
- ( Spread | Variability ) of a sample
- Stacking
- Standard Deviation (SD|s| |RMS width)
- Standard Error (SE)
- (Normalize|Standardize)
- Statistic
- Forward and Backward Stepwise (Selection|Regression)
- (Supervised|Directed) Learning ( Training ) (Problem)
- Support Vector Machines (SVM) algorithm
- Singular Value Decomposition (SVD)
- (Student's) t-test (Mean Comparison)
- t-distributions
- Tail
- (Machine|Statistical) Learning - (Target|Learned|Outcome|Dependent|Response) (Attribute|Variable) (Y|DV)
- Test
- (Test|Expected|Generalization) Error
- Test Set
- (Threshold|Cut-off) of binary classification
- Titanic Data Set
- Training Error
- Training (Data|Set)
- Nested (Transactional|Historical) Data
- Transform
- Treatments (Combination of factor level)
- True score (Classical test theory)
- (True Function|Truth)
- (Total) Sum of the square (TSS|SS)
- Tuning Parameter
- (two class|binary) classification problem (yes/no, false/true)
- Statistical Learning - Two-fold validation
- Data - Uncertainty
- Uniform Distribution (platykurtic)
- Unsupervised Learning ( Mining )
- Resampling through Random Percentage Split
- Validity (Valid Measures)
- (Variance|Dispersion|Mean Square) (MS)
- Variation (Change?)
- Probability and Vizualization
- Statistics vs (Machine Learning|Data Mining)
- Random Walk
- (Golf|Weather) Data Set
- Z Scale
- Z Score (Zero Mean) or Standard Score