Tabla de Contenidos: “…-- Types of dataset and splits -- Cross-validation -- Common evaluation metrics -- Confusion matrix -- ROC -- Precision-Recall curve -- Relation between the ROC curve and PR curve -- Challenges and considerations when dealing with imbalanced data -- When can we have an imbalance in datasets? …”

Tabla de Contenidos: “…2.5.2 Fitting Nonlinear Models -- 2.5.3 Fitting Higher-Order Polynomials -- 2.6 Bland-Altman Analysis -- 2.6.1 The Bland-Altman Plot -- 2.7 Summary -- 2.8 Descriptive Bivariate and Multivariate Statistics Using MATLAB -- 2.8.1 Visualizing Bivariate Data -- 2.8.2 Covariance -- 2.8.3 Correlation -- 2.8.4 Calculating Best-Fit Lines -- 2.8.5 Bland-Altman Analysis -- 2.9 Further Resources -- 2.10 Exercises -- 3 Descriptive Statistics III: ROC Analysis -- 3.1 Introduction -- 3.2 Notation -- 3.2.1 Sensitivity and Speci city -- 3.2.2 Positive and Negative Predictive Values -- 3.2.3 Example Calculation of Se, Sp, PPV and NPV -- 3.3 ROC Curves -- 3.4 Summary -- 3.5 Using MATLAB for ROC Analysis -- 3.6 Further Resources -- 3.7 Exercises -- 4 Inferential Statistics I: Basic Concepts -- 4.1 Introduction -- 4.2 Notation -- 4.3 Probability -- 4.3.1 Probabilities of Single Events -- 4.3.2 Probabilities of Multiple Events -- 4.4 Probability Distributions -- 4.4.1 The Normal Distribution -- 4.5 Why the Normal Distribution Is so Important: The Central Limit Theorem -- 4.6 Standard Error of the Mean -- 4.7 Con dence Intervals of the Mean -- 4.8 Summary -- 4.9 Probability Distributions and Measures of Reliability Using MATLAB -- 4.9.1 Probability Distributions -- 4.9.2 Standard Error of the Mean -- 4.9.3 Con dence Interval of the Mean -- 4.10 Further Resources -- 4.11 Exercises -- 5 Inferential Statistics II: Parametric Hypothesis Testing -- 5.1 Introduction -- 5.2 Hypothesis Testing -- 5.3 Types of Data for Hypothesis Tests -- 5.4 The t-distribution and Student's t-test -- 5.5 One-Sample Student's t-test -- 5.6 Con dence Intervals for Small Samples -- 5.7 Two Sample Student's t-test -- 5.7.1 Paired Data -- 5.7.2 Unpaired Data -- 5.7.3 Paired vs. …”

Tabla de Contenidos: “…Half Title; Title Page; Copyright; Copyright Permissions; Contents; Contributing Authors; Foreword; Preface; Book Organization; Part I: Theory; Part II: Adaptations; Part III: Applications; Companion Website; Contacting Us; Acknowledgments; Part I: Theory; 1 The Basic Conformal Prediction Framework; 1.1 The Basic Setting and Assumptions; 1.2 Set and Confidence Predictors; 1.2.1 Validity and Efficiency of Set and Confidence Predictors; 1.3 Conformal Prediction; 1.3.1 The Binary Case; 1.3.2 The Gaussian Case; 1.4 Efficiency in the Case of Prediction without Objects…”

Tabla de Contenidos: “…; 1.2 Features, Feature Vectors, and Classifiers; 1.3 Supervised, Unsupervised, and Semi-Supervised Learning; 1.4 MATLAB Programs; 1.5 Outline of The Book; Chapter 2 Classifiers Based on Bayes Decision Theory; 2.1 Introduction; 2.2 Bayes Decision Theory; 2.3 Discriminant Functions and Decision Surfaces; 2.4 Bayesian Classification for Normal Distributions; 2.5 Estimation of Unknown Probability Density Functions; 2.6 The Nearest Neighbor Rule; 2.7 Bayesian Networks…”

Tabla de Contenidos: “…Technical requirements -- Understanding text clustering -- Preprocessing the data -- Using speech-to-text -- Introducing the K-means algorithm -- Putting K-means into action -- Introducing DBSCAN -- Putting DBSCAN into action -- Assessing DBSCAN -- Introducing the hierarchical clustering algorithm -- Putting hierarchical clustering into action -- Introducing the LDA algorithm -- Putting LDA into action -- Summary -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…509636_1_En_OFC -- Contents -- Editors and Contributors -- 1 About This Book -- Abstract -- 1 Introduction -- 2 What is the Main Aim of This Book? …”

Tabla de Contenidos: “…Visualizing Model Performance -- Fundamental concepts: Visualization of model performance under various kinds of uncertainty; Further consideration of what is desired from data mining results -- Exemplary techniques: Profit curves; Cumulative response curves; Lift curves; ROC curves -- Ranking Instead of Classifying -- Profit Curves -- ROC Graphs and Curves -- The Area Under the ROC Curve (AUC) -- Cumulative Response and Lift Curves -- Example: Performance Analytics for Churn Modeling -- Summary -- 9. …”

Tabla de Contenidos: “…Parameters and hyperparameters -- Bias initialization -- Hyperparameters -- Use case - digit recognizer -- Generative adversarial networks -- Hinton's Capsule network -- The Capsule Network and convolutional neural networks -- Summary -- Chapter 10: Causal Inference -- Granger causality -- F-test -- Limitations -- Use case -- Graphical causal models -- Summary -- Chapter 11: Advanced Methods -- Introduction -- Kernel PCA -- Independent component analysis -- Preprocessing for ICA -- Approach -- Compressed sensing -- Our goal -- Self-organizing maps -- SOM -- Bayesian multiple imputation -- Summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…-- Bibliography -- References -- R package references -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…-- Understanding Odds -- Logit Function -- Sigmoid Curve -- Using the Breast Cancer Wisconsin (Diagnostic) Data Set -- Examining the Relationship Between Features -- Plotting the Features in 2D -- Plotting in 3D -- Training Using One Feature -- Finding the Intercept and Coefficient -- Plotting the Sigmoid Curve -- Making Predictions -- Training the Model Using All Features -- Testing the Model -- Getting the Confusion Matrix -- Computing Accuracy, Recall, Precision, and Other Metrics -- Receiver Operating Characteristic (ROC) Curve -- Plotting the ROC and Finding the Area Under the Curve (AUC) -- Summary -- Chapter 8 Supervised Learning-Classification Using Support Vector Machines -- What Is a Support Vector Machine? …”