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Statistics - Hypothesis (Tests|Testing)
How to unit test machine learning code

data_mining/test.txt · Last modified: 2017/11/01 14:22 by gerardnico

(Statistics|Probability|Machine Learning|Data Mining|Data and Knowledge Discovery|Pattern Recognition|Data Science|Data Analysis) 302 pages

The 1 Percent Rule

(Absolute|True) Zero

(Parameters|Model) (Accuracy|Precision|Fit|Performance) Metrics

Akaike information criterion (AIC)

(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)

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)

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

(Case-control|retrospective) sampling

Causation - Causality (Cause and Effect) Relationship

Cumulative Distribution Function (CDF)

Centering Continous Predictors

Central limit theorem (CLT)

Centroid (center of gravity)

Customer - Churn Analysis (Customer retention)

(Class|Category|Label) Target

(Classifier|Classification Function)

Clustering (Function|Model)

(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

Correlation (Coefficient analysis)

Cosine Similarity (Measure of Angle)

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 (Preparation | Wrangling | Munging)

Decision Tree (DT) Algorithm

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)

Deviation Score (for one observation)

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

(Probability|Sampling) Distribution

Dummy (Coding|Variable) - One-hot-encoding (OHE)

Effects (between predictor variable)

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|...)

Data analysis - Explanatory

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)

(Frequent itemsets|co-occurring items)

Data Model - Fudge factor

Fuzzy Logic (Partial Truth)

Generalized additive model (GAM)

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)

Hierarchical Clustering

High Dimension (Curse of Dimensionality)

Data Science - History

Homoscedasticity

Hypothesis (Tests|Testing)

Intrusion detection systems (IDS)

Image classification

Independent t-test

Statistical - Inference

Information Gain

Information Retrieval

(Interaction|Synergy) effect

Intercept - Regression (coefficient|constant) <math>B_0</math>

Model Interpretation

(Interval|Delta) (Measurement)

Java API for data mining (JDM)

K-Means Clustering algorithm

K-Nearest Neighbors (KNN) algorithm - Instance based learning

Knots (Cut points)

Kurtosis (Distribution Tail extremity)

Statistical Learning - Lasso

Standard Least Squares Fit (Guassian linear model)

Leptokurtic distribution

(Life cycle|Project|Data Pipeline)

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

Probability mass function (PMF)

Maximum Entropy Algorithm

Maximum likelihood

(Scales of measurement|Type of variables)

(Missing Value|Not Available)

Model vs Expert

Moderator Variable (Z) - Moderation

Monte Carlo (method|experiment) (stochastic process simulations)

(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

(One|Simple) Rule - (One Level Decision Tree)

(Overfitting|Overtraining|Robust|Generalization) (Underfitting)

Data Science - Over-generalization

(Paretian|Power law) distribution

Pareto ( Principle | Distribution )

Principal Component (Analysis|Regression) (PCA)

(Probability) Density Function (PDF)

Mathematics - Permutation (Ordered Combination)

Piecewise polynomials

Partial least squares (PLS)

Predictive Model Markup Language (PMML)

Poisson (Process|distribution)

(Global) Polynomial Regression (Degree)

Population Parameter

Power of a test

(Prediction|Guess)

Predictive Model Markup Language (PMML)

(Machine|Statistical) Learning - (Predictor|Feature|Regressor|Characteristic) - (Independent|Explanatory) Variable (X)

Privacy (Anonymization)

Probit Regression (probability on binary problem)

Process control (SPC)

Pruning (a decision tree, decision rules)

R-squared (<math>R^2</math>|Coefficient of determination) for Model Accuracy

Random Variable

(Fraction|Ratio|Percentage|Share) (Variable|Measurement)

(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)

(Residual|Error Term|Prediction error|Deviation) (e|<math>\epsilon</math>)

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 Distribution

Scoring (Applying)

(Shrinkage|Regularization) of Regression Coefficients

Signal (Wanted Variation)

Significance level

(Significance | Significant) Effect

(Univariate|Simple) Logistic regression

(Univariate|Simple|Basic) Linear Regression

Skew (-ed Distribution|Variable)

( Spread | Variability ) of a sample

Standard Deviation (SD|s|<math>\sigma</math>|RMS width)

Standard Error (SE)

(Normalize|Standardize)

Forward and Backward Stepwise (Selection|Regression)

(Stochastic|random) process

(Supervised|Directed) Learning ("Training") (Problem)

Support Vector Machines (SVM) algorithm

Singular Value Decomposition (SVD)

(Student's) t-test (Mean Comparison)

T-distributions

(Machine|Statistical) Learning - (Target|Learned|Outcome|Dependent|Response) (Attribute|Variable) (Y|DV)

(Test|Expected|Generalization) Error

(Threshold|Cut-off) of binary classification

Titanic Data Set

Training (Data|Set)

Nested (Transactional|Historical) Data

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

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)

(Golf|Weather) Data Set

Z Score (Zero Mean) or Standard Score

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Data (State)

Data Processing
Data Modeling
Data Quality
Data Structure
Data Type
Data Warehouse
Data Visualization
Data Partition
Data Persistence
Data Concurrency

Data Type

Number
Time
Text
Collection
Relation (Table)
Tree
Key/Value
Graph
Spatial
Color

Measure Levels

Order
Nominal
Discrete
Distance
Ratio

Code

Compiler
Lexical Parser
Grammar
Function
Testing
Shipping
Data Type
Versioning

System

Operating System
Process (Thread)
Security
File System

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