Tabla de Contenidos: “…4.1 Introduction -- Parallelizing Computational Tasks -- Parallelizing for Distributed Data -- 4.2 Parallel Processing Approaches -- 4.2.1 Parallel Algorithms for Text Summarization -- 4.2.2 Parallel Bisection k-Means Method -- 4.3 Parallel Data Processing Algorithms for Large-Scale Summarization -- 4.3.1 Designing MapReduce Algorithm for Text Summarization -- 4.3.2 Key Concepts in Mapper -- 4.3.3 Key Concepts in Reducer -- 4.3.4 Summary Generation -- An Illustrative Example for MapReduce -- Good Time: Movie Review -- 4.4 Other MR-Based Methods -- 4.5 Summary -- 4.6 Examples -- K-Means Clustering Using MapReduce -- Parallel LDA Example (Using Gensim Package) -- Sample Code: (Using Gensim Package) -- Example: Creating an Inverted Index -- Example: Relational Algebra (Table JOIN) -- References -- Sample Code -- Chapter 5 Optimization Approaches for Text Summarization -- 5.1 Introduction -- 5.2 Optimization for Summarization -- 5.2.1 Modeling Text Summarization as Optimization Problem -- 5.2.2 Various Approaches for Optimization -- 5.3 Formulation of Various Approaches -- 5.3.1 Sentence Ranking Approach -- 5.3.1.1 Stages and Illustration -- 5.3.2 Evolutionary Approaches -- 5.3.2.1 Stages -- 5.3.2.2 Demonstration -- 5.3.3 MapReduce-Based Approach -- 5.3.3.1 In-Node Optimization Illustration -- 5.3.4 Multi-objective-Based Approach -- Summary -- Exercises -- References -- Sample Code -- Chapter 6 Performance Evaluation of Large-Scale Summarization Systems -- 6.1 Evaluation of Summaries -- 6.1.1 CNN Dataset -- 6.1.2 Daily Mail Dataset -- 6.1.3 Description -- 6.2 Methodologies -- 6.2.1 Intrinsic Methods -- 6.2.2 Extrinsic Methods -- 6.3 Intrinsic Methods -- 6.3.1 Text Quality Measures -- 6.3.1.1 Grammaticality -- 6.3.1.2 Non-redundancy -- 6.3.1.3 Reverential Clarity -- 6.3.1.4 Structure and Coherence -- 6.3.2 Co-selection-Based Methods…”

Tabla de Contenidos: “…Identification of Observed Variable (Path) Models -- General Requirements -- Unique Estimates -- Rule for Recursive Models -- Identification of Nonrecursive Models -- Models with Feedback Loops and All Possible Disturbance Correlations -- Graphical Rules for Other Types of Nonrecursive Models -- Respecification of Nonrecursive Models that are Not Identified -- A Healthy Perspective on Identification -- Empirical Underidentification -- Managing Identification Problems -- Path Analysis Research Example -- Summary -- Learn More -- Exercises -- Appendix 7.A. Evaluation of the Rank Condition -- 8. Graph Theory and the Structural Causal Model -- Introduction to Graph Theory -- Elementary Directed Graphs and Conditional Independences -- Implications for Regression Analysis -- d-Separation -- Basis Set -- Causal Directed Graphs -- Testable Implications -- Graphical Identification Criteria -- Instrumental Variables -- Causal Mediation -- Summary -- Learn More -- Exercises -- Appendix 8.A. …”

Tabla de Contenidos: “…3.2.1.3 Regression Plots of Tails -- 3.2.1.4 The Empirical Quantile Function -- 3.2.2 Density Estimation Based Tools -- 3.2.2.1 The Histogram -- 3.2.2.2 The Kernel Density Estimator -- 3.3 Univariate Parametric Models -- 3.3.1 The Normal and Log‐normal Models -- 3.3.1.1 The Normal and Log‐normal Distributions -- 3.3.1.2 Modeling Stock Prices -- 3.3.2 The Student Distributions -- 3.3.2.1 Properties of Student Distributions -- 3.3.2.2 Estimation of the Parameters of a Student Distribution -- 3.4 Tail Modeling -- 3.4.1 Modeling and Estimating Excess Distributions -- 3.4.1.1 Modeling Excess Distributions -- 3.4.1.2 Estimation -- 3.4.2 Parametric Families for Excess Distributions -- 3.4.2.1 The Exponential Distributions -- 3.4.2.2 The Pareto Distributions -- 3.4.2.3 The Gamma Distributions -- 3.4.2.4 The Generalized Pareto Distributions -- 3.4.2.5 The Weibull Distributions -- 3.4.2.6 A Three Parameter Family -- 3.4.3 Fitting the Models to Return Data -- 3.4.3.1 S&amp -- P 500 Daily Returns: Maximum Likelihood -- 3.4.3.2 Tail Index Estimation for S&amp -- P 500 Components -- 3.5 Asymptotic Distributions -- 3.5.1 The Central Limit Theorems -- 3.5.1.1 Sums of Independent Random Variables -- 3.5.1.2 Sums of Independent and Identically Distributed Random Variables -- 3.5.1.3 Sums of Dependent Random Variables -- 3.5.2 The Limit Theorems for Maxima -- 3.5.2.1 Weak Convergence of Maxima -- 3.5.2.2 Extreme Value Distributions -- 3.5.2.3 Convergence to an Extreme Value Distribution -- 3.5.2.4 Generalized Pareto Distributions -- 3.5.2.5 Convergence to a Generalized Pareto Distribution -- 3.6 Univariate Stylized Facts -- Chapter 4 Multivariate Data Analysis -- 4.1 Measures of Dependence -- 4.1.1 Correlation Coefficients -- 4.1.1.1 Linear Correlation -- 4.1.1.2 Spearman's Rank Correlation -- 4.1.1.3 Kendall's Rank Correlation…”

“…Business Intelligence and OLAP systems are no longer limited to the privileged few business analysts: they are being democratized by being shared with the rank and file employee demanding a Relational Database Management System (RDBMS) that is more OLAP-aware. …”

Tabla de Contenidos: “…3.3.1.7 Different statistical method -- 3.3.1.7.1 Central tendency -- Mean -- Why do not use the mean -- Median -- Mode -- Variance and standard deviation -- Z-score -- Quartiles -- Percentile -- 3.4 Measurement and scaling concepts -- 3.4.1 Comparative scales -- 3.4.1.1 Paired comparison scale -- 3.4.1.2 Rank order scale -- 3.4.1.3 Constant sum scale -- 3.4.1.4 Q-sort scale -- 3.4.2 Non-comparative scales -- 3.4.2.1 Continuous rating scale -- 3.4.2.2 Itemized rating scale -- 3.4.2.2.1 Likert scale -- 3.4.2.2.2 Stapel scale -- 3.4.2.2.3 Semantic differential scale -- 3.5 Various types of scale -- 3.5.1 Nominal -- 3.5.2 Ordinal -- 3.5.3 Interval -- 3.5.4 Ratio -- 3.6 Primary data analysis with Python -- 3.7 Conclusion -- 3.8 Case study -- 3.8.1 Case study: taxonomy of data in a healthcare organization -- 3.8.2 Case study: taxonomy of data in the automobile industry -- 3.8.3 Case study on the data theory -- 3.9 Exercise -- 3.9.1 Objective type question -- 3.9.2 Descriptive type question -- Further reading -- 4 Multivariate data analytics and cognitive analytics -- Abbreviations -- 4.1 Introduction -- 4.2 Factor analytics -- 4.3 Principal component analytics -- 4.4 Cluster analytics -- 4.4.1 K-means -- 4.4.1.1 Algorithms -- 4.4.1.2 K-means clustering -- 4.1.2.1 Steps of the K-means clustering algorithm -- 4.1.2.2 Practice problems based on K-means clustering algorithm -- 4.4.2 Cluster analysis of driverless car dataset -- 4.4.2.1 Problem -- 4.5 Linear regression analysis -- 4.5.1 Mathematical expression for regression analysis -- 4.5.2 Solved example of linear regression analysis of driverless car -- 4.5.2.1 Problem -- 4.5.2.2 Solution -- 4.6 Logistic regression analysis -- 4.7 Application of analytics across value chain -- 4.8 Multivariate data analytics with Python -- 4.9 Conclusion -- 4.10 Case study…”

Tabla de Contenidos: “…-- International Trends in Leadership -- How the Countries Ranked -- Emotional Intelligence and the GLOBE Theory -- Leadership Orientations and Emotional Intelligence -- Chapter 16: Entrepreneurial Leadership and EQ -- The EQ Leader: Entrepreneurial CEOs -- Succeeding in a Small Enterprise: Success Running a Dentist Office -- What Does It Take to Be an Entrepreneur? …”

Tabla de Contenidos: “…. -- ChatGPT-Assisted Risk Ranking and Prioritization -- Getting ready -- How to do it... -- How it works... -- There's more... -- Building Risk Assessment Reports -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 5: Security Awareness and Training -- Technical requirement -- Developing Security Awareness Training Content -- Getting ready -- How to do it... -- How it works... -- There's more... -- Assessing Cybersecurity Awareness -- Getting ready -- How to do it... -- How it works... -- There's more...…”

Tabla de Contenidos: “…4.4.1 Signal Detection in Additive Zero-Median Noise: The Sign Test, 124 -- 4.4.2 Signal Detection in Additive Symmetric Noise: The Rank Test, 125 -- 4.4.3 Signal Detection in Additive Zero Median, Zero Mean, Finite-Variance Noise: The t-Test, 126 -- 4.5 Summary, 127 -- 5 Introduction to Estimation Theory 132 -- 5.1 Introduction, 132 -- 5.2 Random Parameter Estimation: Bayesian Estimation, 134 -- 5.2.1 Minimum Mean-Squared Error Estimation, 134 -- 5.2.2 MMSE Estimation of Vector Parameters, 135 -- 5.2.3 Linear Minimum Mean-Squared Error Estimation, 138 -- 5.2.4 Maximum A Posteriori Probability Estimation, 139 -- 5.3 Nonrandom Parameter Estimation, 140 -- 5.3.1 Theory of Minimum Variance Unbiased Estimation, 142 -- 5.3.2 Best Linear Unbiased Estimator, 147 -- 5.3.3 Maximum Likelihood Estimation, 152 -- 5.3.4 Performance Bounds: Cramer-Rao Lower Bound, 154 -- 5.4 Summary, 158 -- 6 Power Spectrum Estimation 164 -- 6.1 Introduction, 164 -- 6.2 PSD Estimation of a Stationary Discrete-Time Signal, 168 -- 6.2.1 Correlogram Method, 168 -- 6.2.2 Periodogram Method, 170 -- 6.2.3 Performance of the Periodogram PSD Estimate, 172 -- 6.3 Blackman / Tukey Estimator of the Power Spectrum, 177 -- 6.4 Other PSD Estimators Based on Modified Periodograms, 181 -- 6.4.1 Bartlett PSD Estimator, 181 -- 6.4.2 Welch PSD Estimator, 183 -- 6.5 PSD Estimation of Nonstationary Discrete-Time Signals, 186 -- 6.5.1 Temporally Windowed Observations, 188 -- 6.5.2 Temporal and Spectral Smoothing of PSD Estimates of Nonstationary Discrete-Time Signals, 189 -- 6.5.3 DFT-Based PSD Computation, 191 -- 6.6 Spectral Correlation of Cyclostationary Signals, 192 -- 6.6.1 Spectral Correlation and Spectral Autocoherence, 196 -- 6.6.2 Time-Averaged Spectral Correlation, 197 -- 6.6.3 Estimation of Spectral Correlation, 198 -- 6.7 Summary, 200 -- 7 Markov Decision Processes 207 -- 7.1 Introduction, 207 -- 7.2 Markov Decission Processes, 209 -- 7.3 Finite-Horizon MDPs, 212 -- 7.3.1 Definitions, 212 -- 7.3.2 Optimal Policies for MDPs, 216.…”

Tabla de Contenidos: “…3.4 Precision of Monte Carlo Method -- 3.4.1 Monitoring Mean and Variance -- 3.4.2 Importance Sampling -- 3.4.3 Correlated Samples -- 3.4.4 Variance Reduction Methods -- 3.5 Markov Chains -- 3.5.1 Markov Processes -- 3.5.2 Discrete Time, Discrete State Space -- 3.5.3 Transition Probability -- 3.5.4 "Sun City" -- 3.5.5 Utility Bills -- 3.5.6 Classification of States -- 3.5.7 Stationary Distribution -- 3.5.8 Reversibility Condition -- 3.5.9 Markov Chains with Continuous State Spaces -- 3.6 Simulation of a Markov Chain -- 3.7 Applications -- 3.7.1 Bank Sizes -- 3.7.2 Related Failures of Car Parts -- References -- 4 Markov Chain Monte Carlo Methods -- 4.1 Markov Chain Simulations for Sun City and Ten Coins -- 4.2 Metropolis-Hastings Algorithm -- 4.3 Random Walk MHA -- 4.4 Gibbs Sampling -- 4.5 Diagnostics of MCMC -- 4.5.1 Monitoring Bias and Variance of MCMC -- 4.5.2 Burn-in and Skip Intervals -- 4.5.3 Diagnostics of MCMC -- 4.6 Suppressing Bias and Variance -- 4.6.1 Perfect Sampling -- 4.6.2 Adaptive MHA -- 4.6.3 ABC and Other Methods -- 4.7 Time-to-Default Analysis of Mortgage Portfolios -- 4.7.1 Mortgage Defaults -- 4.7.2 Customer Retention and Infinite Mixture Models -- 4.7.3 Latent Classes and Finite Mixture Models -- 4.7.4 Maximum Likelihood Estimation -- 4.7.5 A Bayesian Model -- References -- PART II Modeling Dependence -- 5 Statistical Dependence Structures -- 5.1 Introduction -- 5.2 Correlation -- 5.2.1 Pearson's Linear Correlation -- 5.2.2 Spearman's Rank Correlation -- 5.2.3 Kendall's Concordance -- 5.3 Regression Models -- 5.3.1 Heteroskedasticity -- 5.3.2 Nonlinear Regression -- 5.3.3 Prediction -- 5.4 Bayesian Regression -- 5.5 Survival Analysis -- 5.5.1 Proportional Hazards -- 5.5.2 Shared Frailty -- 5.5.3 Multistage Models of Dependence -- 5.6 Modeling Joint Distributions -- 5.6.1 Bivariate Survival Functions -- 5.6.2 Bivariate Normal…”

Tabla de Contenidos: “…Loading the libraries -- Understanding and preparing the data -- Assessing time series models with traditional interpretation methods -- Generating LSTM attributions with integrated gradients -- Computing global and local attributions with SHAP's KernelExplainer -- Identifying influential features with factor prioritization -- Quantifying uncertainty and cost sensitivity with factor fixing -- Mission accomplished -- Summary -- Dataset and image sources -- References -- Section 3: Tuning for Interpretability -- Chapter 10: Feature Selection and Engineering for Interpretability -- Technical requirements -- The mission -- The approach -- The preparations -- Loading the libraries -- Understanding and preparing the data -- Understanding the effect of irrelevant features -- Reviewing filter-based feature selection methods -- Basic filter-based methods -- Correlation filter-based methods -- Ranking filter-based methods -- Comparing filter-based methods -- Exploring embedded feature selection methods -- Discovering wrapper, hybrid, and advanced feature selection methods -- Wrapper methods -- Hybrid methods -- Advanced methods -- Evaluating all feature-selected models -- Considering feature engineering -- Mission accomplished -- Summary -- Dataset sources -- Further reading -- Chapter 11: Bias Mitigation and Causal Inference Methods -- Technical requirements -- The mission -- The approach -- The preparations -- Loading the libraries -- Understanding and preparing the data -- Detecting bias -- Visualizing dataset bias -- Quantifying dataset bias -- Quantifying model bias -- Mitigating bias -- Pre-processing bias mitigation methods -- In-processing bias mitigation methods -- Post-processing bias mitigation methods -- Tying it all together! …”

Tabla de Contenidos: “…Performance evaluation -- Distributed training on AWS deep learning AMI 9.0 -- Frequently asked questions (FAQs) -- Summary -- Answers to questions -- Chapter 9: Playing GridWorld Game Using Deep Reinforcement Learning -- Notation, policy, and utility for RL -- Notations in reinforcement learning -- Policy -- Utility -- Neural Q-learning -- Introduction to QLearning -- Neural networks as a Q-function -- Developing a GridWorld game using a deep Q-network -- Generating the grid -- Calculating agent and goal positions -- Calculating the action mask -- Providing guidance action -- Calculating the reward -- Flattening input for the input layer -- Network construction and training -- Playing the GridWorld game -- Frequently asked questions (FAQs) -- Summary -- Answers to questions -- Chapter 10: Developing Movie Recommendation Systems Using Factorization Machines -- Recommendation systems -- Recommendation approaches -- Collaborative filtering approaches -- Content-based filtering approaches -- Hybrid recommender systems -- Model-based collaborative filtering -- The utility matrix -- The cold-start problem in collaborative-filtering approaches -- Factorization machines in recommender systems -- Developing a movie recommender system using FMs -- Dataset description and exploratory analysis -- Movie rating prediction -- Converting the dataset into LibFM format -- Training and test set preparation -- Movie rating prediction -- Which one makes more sense -- - ranking or rating? -- Frequently asked questions (FAQs) -- Summary -- Answers to questions -- Chapter 11: Discussion, Current Trends, and Outlook -- Discussion and outlook -- Discussion on the completed projects -- Titanic survival prediction using MLP and LSTM networks -- Cancer type prediction using recurrent type networks -- Image classification using convolutional neural networks…”

Tabla de Contenidos: “…4.6 Discussion -- 4.7 Conclusion -- References -- 5 A Method to Solve Trapezoidal Transshipment Problem under Uncertainty -- 5.1 Introduction -- 5.2 Preliminaries -- 5.3 Arithmetic Operations -- 5.4 Ranking Function -- 5.5 Mathematical Formulation of the Transshipment Problem under Uncertainty -- 5.6 Proposed Method -- 5.7 Numerical Example -- 5.8 Results and Discussion -- 5.9 Comparative Studies -- 5.10 Conclusion -- References -- 6 Enhancing the Security of Public key Cryptographic Model based on Integrated ElGamal-Elliptic Curve Diffe Hellman (EG-ECDH) Key Exchange Technique -- 6.1 Introduction -- 6.2 Generalized Fibonacci Matrix and its Inverse -- 6.3 Technique for key Generation, Encryption and Decryption -- 6.3.1 ElGamal Technique -- 6.3.2 ECDH Technique -- 6.3.3 Proposed Integrated ElGamal-ECDH Technique for key Generation -- 6.4 Technique for Encryption and Decryption -- 6.5 Algorithm for Proposed Model -- 6.6 Numerical Example -- 6.7 Complexity of this Model -- 6.8 Conclusion -- References -- 7 An Investigation of Fractional Ordered Biological Systems using a Robust Semi-Analytical Technique -- 7.1 Introduction -- 7.2 Generalized time Fractional order Biological Population Model -- 7.3 Preliminaries -- 7.3.1 Basic Concept of Fractional Calculus -- 7.3.2 Basic Definitions -- 7.4 Implementation of Fractional Modified Differential Transform Method -- 7.5 Conclusion -- References -- 8 Variable Selection in Multiple Nonparametric Regression Modelling -- 8.1 Introduction -- 8.2 Proposed Methodology and Statistical Properties -- 8.2.1 Description of the Method -- 8.2.2 Large Sample Statistical Property -- 8.3 Simulation Study -- 8.4 Real data Analysis -- 8.4.1 QSAR Fish Toxicity Data Set -- 8.4.2 Istanbul Stock Exchange Data Set -- 8.4.3 SGEMM GPU Kernel Performance Data Set -- 8.4.4 CSM (Conventional and Social Media Movies) Data Set…”

Tabla de Contenidos: “…Step 3: Identify Individual Accomplishments and Their Integration with the Work Unit Goals -- Step 4: Convert Expected Accomplishments into Performance Elements, Duly Mentioning Their Type and Priority -- Step 5: Determine Work Unit and Individual Measures -- Step 6: Develop Work Unit and Individual Standards -- Step 7: Determine How to Monitor Performance -- Step 8: Check the Performance Plan -- Preparing the Performance Development Plan -- Transition from Individual Performance Plan to Group Performance Plan -- Performance Plan and Role Clarity -- Role Descriptions Template -- Creating Strategic Plans and Their Alignment with the Performance Plans -- Strategy Realization: Essential Elements Through Performance Plans -- Summary -- Key Words -- General Review Questions -- Critical Review Question -- References -- Further Reading -- Case Study -- Chapter 3: Performance Appraisal -- Introduction -- Definitions -- Role of Appraisals in Performance Management -- Process and Methods of Performance Appraisal -- Purposes of Performance Appraisal -- Importance of Performance Appraisal -- Objectives of Performance Appraisal -- Reasons for Failure of Performance Appraisal -- Steps to Performance Appraisal -- Characteristics of an Appraisal System -- Performance Appraisal Design -- Approaches to Performance Appraisal -- Types and Methods of Performance Appraisal -- Traditional Methods -- Straight Ranking Method -- Paired Comparison Techniques -- Man-to-Man Comparison -- Grading Method -- Graphic or Linear Rating Scale -- Example of Graphic Rating Scale -- Forced Choice Description Method -- Forced Distribution Method -- Checklist Method -- Free Easy Method -- Critical Incident Method -- Work Standard Approach -- Group Appraisal Method -- Field Review Method -- Modern Methods -- Appraisal by Results for Management by Objectives -- Advantages -- Disadvantages…”

Tabla de Contenidos: “…-- Getting Familiar with File Formats -- Supported file formats -- Making sense of bit depth -- Understanding color spaces -- Part 2: Managing Your Photos with Lightroom Classic -- Chapter 4: Tackling the Lightroom Classic Import Process -- Knowing How the Import Process Works -- Exploring the Import Dialog -- Importing Your Photos into Lightroom Classic -- Employing an effective import workflow -- Putting it all together -- Auto Import -- Shooting Tethered -- Chapter 5: Viewing and Finding Photos in the Library -- Exploring the Library Module -- Getting to know the panels and tools -- Becoming familiar with the menu options -- Creating a custom identity plate -- Choosing the Right View for the Task -- Working with thumbnails in Grid view -- Taking a closer look in Loupe view -- Using the Panels to Access Your Photos -- Getting the 20,000-foot view from the Catalog panel -- Using the Folders panel like a file browser -- Grouping photos into collections -- Creating Multiple Versions with Virtual Copies -- Chapter 6: Getting Organized with the Library -- Evaluating Photos -- Survey view -- Compare view -- Flags, Ratings, and Color Labels -- Deleting photos from Lightroom Classic -- Using ratings to rank images -- Applying color labels to photos -- Applying color labels to folders and collections -- Filtering folders and collections -- Filenames and Metadata -- Batch renaming with filename templates -- Creating metadata templates to embed information into each image -- Keywording -- Adding and organizing keywords -- Using the Keyword List to find photos -- Using the Painter Tool -- Finding Photos with the Library Filter Bar -- Chapter 7: Exploring the Library Module's Advanced Features -- People View -- Finding faces…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Acknowledgments -- About the Author -- About the Technical Editor -- Contents at a Glance -- Contents -- Introduction -- CC Certification -- Taking the CC Exam -- Computer-Based Testing Environment -- Exam Retake Policy -- Recertification Requirements -- Using the Online Practice Test -- How to Contact the Publisher -- Part I Domain 1: Security Principles -- Chapter 1 Confidentiality, Integrity, Availability, and Non-repudiation: Objective 1.1 Understand the Security Concepts of Information Assurance -- The CIA Triad -- Confidentiality -- Integrity -- Availability -- Non-repudiation -- Chapter 2 Authentication and Authorization: Objective 1.1 Understand the Security Concepts of Information Assurance -- Access Control Process -- Identification -- Authentication -- Authorization -- Accounting -- Digital Access Control -- Password Policies -- Password Length -- Password Complexity -- Password Expiration -- Password History -- Password Resets -- Password Reuse -- Password Managers -- Authentication Factors -- Something You Know -- Something You Are -- Something You Have -- Multi-factor Authentication -- Chapter 3 Privacy: Objective 1.1 Understand the Security Concepts of Information Assurance -- Privacy -- Types of Private Information -- Expectation of Privacy -- Privacy Management Framework -- Management -- Agreement, Notice, and Communication -- Collection and Creation -- Use, Retention, and Disposal -- Access -- Disclosure to Third Parties -- Security for Privacy -- Data Integrity and Quality -- Monitoring and Enforcement -- Chapter 4 Risk Management: Objective 1.2 Understand the Risk Management Process -- Risk Types -- Internal and External Risks -- Multiparty Risks -- Specific Risks -- Risk Identification and Assessment -- The Language of Risk -- Ranking Risks -- Risk Treatment Strategies…”

Tabla de Contenidos: “…Defining a strategy to get it to work with a multivariate time series model -- Laying the groundwork for the permutation approximation strategy -- Computing the SHAP values -- Identifying influential features with factor prioritization -- Computing Morris sensitivity indices -- Analyzing the elementary effects -- Quantifying uncertainty and cost sensitivity with factor fixing -- Generating and predicting on Saltelli samples -- Performing Sobol sensitivity analysis -- Incorporating a realistic cost function -- Mission accomplished -- Summary -- Dataset and image sources -- Further reading -- Chapter 10: Feature Selection and Engineering for Interpretability -- Technical requirements -- The mission -- The approach -- The preparations -- Loading the libraries -- Understanding and preparing the data -- Understanding the effect of irrelevant features -- Creating a base model -- Evaluating the model -- Training the base model at different max depths -- Reviewing filter-based feature selection methods -- Basic filter-based methods -- Constant features with a variance threshold -- Quasi-constant features with value_counts -- Duplicating features -- Removing unnecessary features -- Correlation filter-based methods -- Ranking filter-based methods -- Comparing filter-based methods -- Exploring embedded feature selection methods -- Discovering wrapper, hybrid, and advanced feature selection methods -- Wrapper methods -- Sequential forward selection (SFS) -- Hybrid methods -- Recursive Feature Elimination (RFE) -- Advanced methods -- Model-agnostic feature importance -- Genetic algorithms -- Evaluating all feature-selected models -- Considering feature engineering -- Mission accomplished -- Summary -- Dataset sources -- Further reading -- Chapter 11: Bias Mitigation and Causal Inference Methods -- Technical requirements -- The mission -- The approach…”

Tabla de Contenidos: “…Cost Matrix -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.9 Ranking and Prioritization -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.10 Cost of Low-Quality Data -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.11 Cost-Benefit Analysis and ROI -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.12 Other Relevant Business Impact Techniques -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4 Summary -- Step 5 Identify Root Causes -- Introduction to Step 5.…”

Tabla de Contenidos: “…-- Darwinian evolution -- The genetic algorithms analogy -- The theory behind genetic algorithms -- The schema theorem -- Differences from traditional algorithms -- Population-based -- Genetic representation -- Fitness function -- Probabilistic behavior -- Advantages of genetic algorithms -- Global optimization -- Handling complex problems -- Handling a lack of mathematical representation -- Resilience to noise -- Parallelism -- Continuous learning -- Limitations of genetic algorithms -- Special definitions -- Hyperparameter tuning -- Computationally intensive -- Premature convergence -- No guaranteed solution -- Use cases for genetic algorithms -- Summary -- Further reading -- Chapter 2: Understanding the Key Components of Genetic Algorithms -- The basic flow of a genetic algorithm -- Creating the initial population -- Calculating the fitness -- Applying selection, crossover, and mutation -- Checking the stopping conditions -- Selection methods -- Roulette wheel selection -- Stochastic universal sampling -- Rank-based selection -- Fitness scaling -- Tournament selection -- Crossover methods -- Single-point crossover -- Two-point and k-point crossover -- Uniform crossover -- Crossover for ordered lists -- Mutation methods -- Flip-bit mutation -- Swap mutation -- Inversion mutation -- Scramble mutation -- Real-coded genetic algorithms -- Blend crossover -- Simulated binary crossover -- Real mutation -- Understanding elitism -- Niching and sharing -- Serial niching versus parallel niching -- The art of solving problems using genetic algorithms -- Summary -- Further reading -- Part 2: Solving Problems with Genetic Algorithms…”