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0 (hodnocen0 x )

BK

Second edition

Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo : O’Reilly Media, Inc., 2020

xvi, 342 stran : ilustrace ; 24 cm

ISBN 978-1-4920-7294-2 (brožováno)

Obsahuje bibliografii a rejstřík

001951511

Preface xiii // 1. Exploratory Data Analysis 1 // Elements of Structured Data 2 // Further Reading 4 // Rectangular Data 4 // Data Frames and Indexes 6 // Nonrectangular Data Structures 6 // Further Reading 7 // Estimates of Location 7 // Mean 9 // Median and Robust Estimates 10 // Example: Location Estimates of Population and Murder Rates 12 // Further Reading 13 // Estimates of Variability 13 // Standard Deviation and Related Estimates 14 // Estimates Based on Percentiles 16 // Example: Variability Estimates of State Population 18 // Further Reading 19 // Exploring the Data Distribution 19 // Percentiles and Boxplots 20 // Frequency Tables and Histograms 22 // Density Plots and Estimates 24 // Further Reading 26 // Exploring Binary and Categorical Data 27 // Mode 29 // Expected Value 29 // Probability 30 // Further Reading 30 // Correlation 30 // Scatterplots 34 // Further Reading 36 // Exploring Two or More Variables 36 // Hexagonal Binning and Contours (Plotting Numeric Versus Numeric Data) 36 Two Categorical Variables 39 // Categorical and Numeric Data 41 // Visualizing Multiple Variables 43 // Further Reading 46 // Summary 46 // 2. Data and Sampling Distributions 47 // Random Sampling and Sample Bias 48 // Bias 50 // Random Selection 51 // Size Versus Quality: When Does Size Matter? 52 // Sample Mean Versus Population Mean 53 // Further Reading 53 // Selection Bias 54 // Regression to the Mean 55 // Further Reading 57 // Sampling Distribution of a Statistic 57 // Central Limit Theorem 60 // Standard Error 60 // Further Reading 61 // The Bootstrap 61 // Resampling Versus Bootstrapping 65 // Further Reading 65 // Confidence Intervals 65 // Further Reading 68 // Normal Distribution 69 // Standard Normal and QQ-Plots 71 // Long-Tailed Distributions 73 // Further Reading 75 // Students t-Distribution 75 // Further Reading 78 // Binomial Distribution 78 // Further Reading 81 //

F-Distribution 82 // Further Reading 82 // Poisson and Related Distributions 82 // Poisson Distributions 83 // Exponential Distribution 84 // Estimating the Failure Rate 84 // Weibull Distribution 85 // Further Reading 86 // Summary 86 // 3. Statistical Experiments and Significance Testing 87 // A/? Testing 88 // Why Have a Control Group? 90 // Why Just A/?? Why Not C, D, ? 91 // Further Reading 92 // Hypothesis Tests 93 // The Null Hypothesis 94 // Alternative Hypothesis 95 // One-Way Versus Two-Way Hypothesis Tests 95 // Further Reading 96 // Resampling 96 // Permutation Test 97 // Example: Web Stickiness 98 // Exhaustive and Bootstrap Permutation Tests 102 // Permutation Tests: The Bottom Line for Data Science 102 // Further Reading 103 // Statistical Significance and p-Values 103 // p-Value 106 // Alpha 107 // Type 1 and Type 2 Errors 109 // Data Science and p-Values 109 // Further Reading 110 // t-Tests 110 // Further Reading 112 // Multiple Testing 112 // Further Reading 116 // Degrees of Freedom 116 // Further Reading 118 // ANO VA 118 // F-Statistic 121 // Two-Way ANOVA 123 // Further Reading 124 // Chi-Square Test 124 // Further Reading Summary // 236 // 236 // Statistical Machine Learning // ?-Nearest Neighbors 238 // A Small Example: Predicting Loan Default 239 // Distance Metrics 241 // One Hot Encoder 242 // Standardization (Normalization, z-Scores) 243 // Choosing ? 246 // KNN as a Feature Engine 247 // Tree Models 249 // A Simple Example 250 // The Recursive Partitioning Algorithm 252 // Measuring Homogeneity or Impurity 254 // Stopping the Tree from Growing 256 // Predicting a Continuous Value 257 // How Trees Are Used 258 // Further Reading 259 // Bagging and the Random Forest 259 // Bagging 260 // Random Forest 261 // Variable Importance 265 // Hyperparameters 269 // Boosting 270 // The Boosting Algorithm 271 // XGBoost 272 //

Regularization: Avoiding Overfitting 274 // Hyperparameters and Cross-Validation 279 // Summary 282 // Unsupervised Learning // Principal Components Analysis 284 // A Simple Example 285 // Computing the Principal Components 288 // Interpreting Principal Components 289 // Correspondence Analysis 292 // Further Reading 294 // ?-Means Clustering 294 // A Simple Example 295 // ?-Means Algorithm 298 // Interpreting the Clusters 299 // Selecting the Number of Clusters 302 // Hierarchical Clustering 304 // A Simple Example 305 // The Dendrogram 306 // The Agglomerative Algorithm 308 // Measures of Dissimilarity 309 // Model-Based Clustering 311 // Multivariate Normal Distribution 311 // Mixtures of Normals 312 // Selecting the Number of Clusters 315 // Further Reading 318 // Scaling and Categorical Variables 318 // Scaling the Variables 319 // Dominant Variables 321 // Categorical Data and Gower s Distance 322 // Problems with Clustering Mixed Data 325 // Summary 326 // Bibliography 327 // Index 329