Statistics - Bimodal Distribution
Table of Contents
About
A bimodal distribution has two modes.
- 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-variance trade-off (between overfitting and underfitting)
- Bias (Sampling error)
- 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)
- Confidence Interval
- Statistics - (Confidence|likelihood) (Prediction probabilities|Probability classification)
- 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)
- (Dimension|Feature) (Reduction)
- Dimensionality (number of variable, parameter) (P)
- (Data|Text) Mining - Word-sense disambiguation (WSD)
- (Discretizing|binning) (bin)
- Quadratic discriminant analysis (QDA)
- Discriminant analysis
- (Discriminative|conditional) models
- Distance
- Statistics / Distribution - (Function)
- Dummy (Coding|Variable) - One-hot-encoding (OHE)
- Effect Size
- Effects (between predictor variable)
- Elastic Net Model
- Ensemble Learning (meta set)
- Entropy (Information Gain)
- (Error|misclassification) Rate - false (positives|negatives)
- Prediction Error (Training versus Test)
- (Estimation|Approximation)
- (Estimator|Point Estimate) - Predicted (Score|Target|Outcome| )
- Exponential Distribution
- (F-Statistic|F-test|F-ratio)
- F-distributions
- Face Recognition
- Factor Analysis
- (Factor Variable|Qualitative Predictor)
- Factorial Anova
- Feature Engineering
- (Feature|Attribute) Extraction Function
- Feature Hashing
- (Attribute|Feature) (Selection|Importance)
- Fraud Detection
- Frequency Distribution
- (Frequency|Rate)
- (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
- Fisher (Multiple Linear Discriminant Analysis|multi-variant Gaussian)
- Statistical Learning - Simple Linear Discriminant Analysis (LDA)
- Linear (Regression|Model)
- (Linear spline|Piecewise linear function)
- Little r - (Pearson product-moment Correlation coefficient)
- LOcal (Weighted) regrESSion (LOESS|LOWESS)
- Global vs Local
- 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)
- 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-class (classification|problem)
- Multi-variant logistic regression
- (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)
- Multi-response linear regression (Linear Decision trees)
- Non-linear (effect|function|model)
- Non-Negative Matrix Factorization (NMF) Algorithm
- (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 Coefficient|Weight|Slope) (B)
- Assumptions underlying correlation and regression analysis (Never trust summary statistics alone)
- Regression
- (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 Distribution
- Sampling Error
- Sampling
- (Scales of measurement|Type of variables)
- Scale
- Scoring (Applying)
- (Shrinkage|Regularization) of Regression Coefficients
- Signal (Wanted Variation)
- Significance level
- (Significance | Significant) Effect
- Similarity
- (Univariate|Simple) Logistic regression
- (Univariate|Simple|Basic) Linear Regression
- Simple Effect
- 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)
- t-distributions
- (Student's) t-test (Mean Comparison)
- Tail
- (Machine|Statistical) Learning - (Target|Learned|Outcome|Dependent|Response) (Attribute|Variable) (Y|DV)
- (Test|Expected|Generalization) Error
- Test Set
- Test
- (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