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Machine learning in python : essential techniques for predictive analysis / Michael Bowles
| Machine learning in python : essential techniques for predictive analysis / Michael Bowles |
| Autore | BOWLES, Michael |
| Pubbl/distr/stampa | Indianapolis, : Wiley, 2015 |
| Descrizione fisica | Testo elettronico (PDF)(361 p.) |
| Disciplina | 006.31 |
| Soggetto topico |
Python |
| ISBN | 9781119183600 |
| Formato | Risorse elettroniche  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996453350503316 |
BOWLES, Michael
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| Indianapolis, : Wiley, 2015 | ||
| Risorse elettroniche | ||
| Lo trovi qui: Univ. di Salerno | ||
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Machine learning in python : essential techniques for predictive analysis / / Michael Bowles
| Machine learning in python : essential techniques for predictive analysis / / Michael Bowles |
| Autore | Bowles Michael |
| Pubbl/distr/stampa | Indianapolis, Indiana : , : Wiley, , 2015 |
| Descrizione fisica | 1 recurso en línea (361 p.) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Python (Computer program language) |
| ISBN |
1-118-96175-7
1-118-96176-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Machine Learning in Python®; Contents; Introduction; Chapter 1 The Two Essential Algorithms for Making Predictions; Why Are These Two Algorithms So Useful?; What Are Penalized Regression Methods?; What Are Ensemble Methods?; How to Decide Which Algorithm to Use; The Process Steps for Building a Predictive Model; Framing a Machine Learning Problem; Feature Extraction and Feature Engineering; Determining Performance of a Trained Model; Chapter Contents and Dependencies; Summary; Chapter 2 Understand the Problem by Understanding the Data; The Anatomy of a New Problem
Different Types of Attributes and Labels Drive Modeling Choices Things to Notice about Your New Data Set; Classification Problems: Detecting Unexploded Mines Using Sonar; Physical Characteristics of the Rocks Versus Mines Data Set; Statistical Summaries of the Rocks versus Mines Data Set; Visualization of Outliers Using Quantile-Quantile Plot; Statistical Characterization of Categorical Attributes; How to Use Python Pandas to Summarize the Rocks Versus Mines Data Set; Visualizing Properties of the Rocks versus Mines Data Set; Visualizing with Parallel Coordinates Plots Visualizing Interrelationships between Attributes and Labels Visualizing Attribute and Label Correlations Using a Heat Map; Summarizing the Process for Understanding Rocks versus Mines Data Set; Real-Valued Predictions with Factor Variables: How Old Is Your Abalone?; Parallel Coordinates for Regression Problems-Visualize Variable Relationships for Abalone Problem; How to Use Correlation Heat Map for Regression-Visualize Pair-Wise Correlations for the Abalone Problem; Real-Valued Predictions Using Real-Valued Attributes: Calculate How Your Wine Tastes Multiclass Classification Problem: What Type of Glass Is That?Summary; Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data; The Basic Problem: Understanding Function Approximation; Working with Training Data; Assessing Performance of Predictive Models; Factors Driving Algorithm Choices and Performance-Complexity and Data; Contrast Between a Simple Problem and a Complex Problem; Contrast Between a Simple Model and a Complex Model; Factors Driving Predictive Algorithm Performance; Choosing an Algorithm: Linear or Nonlinear? Measuring the Performance of Predictive Models Performance Measures for Different Types of Problems; Simulating Performance of Deployed Models; Achieving Harmony Between Model and Data; Choosing a Model to Balance Problem Complexity, Model Complexity, and Data Set Size; Using Forward Stepwise Regression to Control Overfitting; Evaluating and Understanding Your Predictive Model; Control Overfitting by Penalizing Regression Coefficients-Ridge Regression; Summary; Chapter 4 Penalized Linear Regression; Why Penalized Linear Regression Methods Are So Useful; Extremely Fast Coefficient Estimation Variable Importance Information |
| Record Nr. | UNINA-9910131307203321 |
|
Bowles Michael
|
||
| Indianapolis, Indiana : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning in python : essential techniques for predictive analysis / / Michael Bowles
| Machine learning in python : essential techniques for predictive analysis / / Michael Bowles |
| Autore | Bowles Michael |
| Pubbl/distr/stampa | Indianapolis, Indiana : , : Wiley, , 2015 |
| Descrizione fisica | 1 recurso en línea (361 p.) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Python (Computer program language) |
| ISBN |
1-118-96175-7
1-118-96176-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Machine Learning in Python®; Contents; Introduction; Chapter 1 The Two Essential Algorithms for Making Predictions; Why Are These Two Algorithms So Useful?; What Are Penalized Regression Methods?; What Are Ensemble Methods?; How to Decide Which Algorithm to Use; The Process Steps for Building a Predictive Model; Framing a Machine Learning Problem; Feature Extraction and Feature Engineering; Determining Performance of a Trained Model; Chapter Contents and Dependencies; Summary; Chapter 2 Understand the Problem by Understanding the Data; The Anatomy of a New Problem
Different Types of Attributes and Labels Drive Modeling Choices Things to Notice about Your New Data Set; Classification Problems: Detecting Unexploded Mines Using Sonar; Physical Characteristics of the Rocks Versus Mines Data Set; Statistical Summaries of the Rocks versus Mines Data Set; Visualization of Outliers Using Quantile-Quantile Plot; Statistical Characterization of Categorical Attributes; How to Use Python Pandas to Summarize the Rocks Versus Mines Data Set; Visualizing Properties of the Rocks versus Mines Data Set; Visualizing with Parallel Coordinates Plots Visualizing Interrelationships between Attributes and Labels Visualizing Attribute and Label Correlations Using a Heat Map; Summarizing the Process for Understanding Rocks versus Mines Data Set; Real-Valued Predictions with Factor Variables: How Old Is Your Abalone?; Parallel Coordinates for Regression Problems-Visualize Variable Relationships for Abalone Problem; How to Use Correlation Heat Map for Regression-Visualize Pair-Wise Correlations for the Abalone Problem; Real-Valued Predictions Using Real-Valued Attributes: Calculate How Your Wine Tastes Multiclass Classification Problem: What Type of Glass Is That?Summary; Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data; The Basic Problem: Understanding Function Approximation; Working with Training Data; Assessing Performance of Predictive Models; Factors Driving Algorithm Choices and Performance-Complexity and Data; Contrast Between a Simple Problem and a Complex Problem; Contrast Between a Simple Model and a Complex Model; Factors Driving Predictive Algorithm Performance; Choosing an Algorithm: Linear or Nonlinear? Measuring the Performance of Predictive Models Performance Measures for Different Types of Problems; Simulating Performance of Deployed Models; Achieving Harmony Between Model and Data; Choosing a Model to Balance Problem Complexity, Model Complexity, and Data Set Size; Using Forward Stepwise Regression to Control Overfitting; Evaluating and Understanding Your Predictive Model; Control Overfitting by Penalizing Regression Coefficients-Ridge Regression; Summary; Chapter 4 Penalized Linear Regression; Why Penalized Linear Regression Methods Are So Useful; Extremely Fast Coefficient Estimation Variable Importance Information |
| Record Nr. | UNINA-9910828667003321 |
|
Bowles Michael
|
||
| Indianapolis, Indiana : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||