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(Statistics|Probability|Machine Learning|Data Mining|Data and Knowledge Discovery|Pattern Recognition|Data Science|Data Analysis) 307 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)

Data-Science - Cheatsheet

(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)

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

Statistics / Distribution - (Function)

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|...)

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)

Data Model - Fudge factor

Fuzzy Logic (Partial Truth)

Generalized additive model (GAM)

Statistics / Gaussian function (<math>e^{-x^2}</math>)

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)

Grouping (Classification)

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)

Function - Maximum (Max)

Maximum Entropy Algorithm

Maximum likelihood

(Missing Value|Not Available)

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

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

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

Data Science - Over-generalization

(Paretian|Power law) distribution

Pareto ( Principle | Distribution )

Pascal Triangle

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 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)

Probability (of an event)

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 (Random quantity|Aleatory variable|Stochastic variable)

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

Recommendations

(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

(Scales of measurement|Type of variables)

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)

(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 (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)

(Golf|Weather) Data Set

Z Score (Zero Mean) or Standard Score

Test

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

1 - List

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

In God we trust, all others must bring data.
W.E. Deming

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