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Regression analysis with Python : learn the art of regression analysis with Python / / Luca Massaron, Alberto Boschetti
| Regression analysis with Python : learn the art of regression analysis with Python / / Luca Massaron, Alberto Boschetti |
| Autore | Massaron Luca |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, , 2016 |
| Descrizione fisica | 1 online resource (312 p.) |
| Collana | Community experience distilled |
| Soggetto topico |
Python (Computer program language)
Regression analysis |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-78398-074-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Regression - The Workhorse of Data Science; Regression analysis and data science; Exploring the promise of data science; The challenge; The linear models; What you are going to find in the book ; Python for data science; Installing Python; Choosing between Python 2 and Python 3; Step-by-step installation; Installing packages; Package upgrades; Scientific distributions; Introducing Jupyter or IPython; Python packages and functions for linear models ; NumPy; SciPy
StatsmodelsScikit-learn; Summary; Chapter 2: Approaching Simple Linear Regression; Defining a regression problem; Linear models and supervised learning; Reflecting on predictive variables; Reflecting on response variables; The family of linear models; Preparing to discover simple linear regression; Starting from the basics; A measure of linear relationship; Extending to linear regression; Regressing with StatsModels; The coefficient of determination; Meaning and significance of coefficients; Evaluating the fitted values; Correlation is not causation; Predicting with a regression model Regressing with Scikit-learnMinimizing the cost function; Explaining the reason for using squared errors; Pseudoinverse and other optimization methods; Gradient Descent at work; Summary; Chapter 3: Multiple Regression in Action; Using multiple features; Model building with Statsmodels; Using formulas as an alternative; The correlation matrix; Revisiting gradient descent; Feature scaling; Unstandardizing coefficients; Estimating feature importance; Inspecting standardized coefficients; Comparing models by R-squared; Interaction models; Discovering interactions; Polynomial regression Testing linear versus cubic transformationGoing for higher-degree solutions; Introducing underfitting and overfitting; Summary; Chapter 4: Logistic Regression; Defining a classification problem; Formalization of the problem: binary classification; Assessing the classifier's performance; Defining a probability-based approach; More on the logistic and logit functions; Let's see some code; Pros and cons of logistic regression; Revisiting Gradient Descend; Multiclass Logistic Regression; An example; Summary; Chapter 5: Data Preparation; Numeric feature scaling; Mean centering; Standardization NormalizationThe logistic regression case; Qualitative feature encoding; Dummy coding with Pandas; DictVectorizer and one-hot encoding; Feature hasher; Numeric feature transformation; Observing residuals; Summarizations by binning; Missing data; Missing data imputation; Keeping track of missing values; Outliers; Outliers on the response; Outliers among the predictors; Removing or replacing outliers; Summary; Chapter 6: Achieving Generalization; Checking on out-of-sample data; Testing by sample split; Cross-validation; Bootstrapping; Greedy selection of features ; The Madelon dataset Univariate selection of features |
| Record Nr. | UNINA-9910511330203321 |
Massaron Luca
|
||
| Birmingham : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Regression analysis with Python : learn the art of regression analysis with Python / / Luca Massaron, Alberto Boschetti
| Regression analysis with Python : learn the art of regression analysis with Python / / Luca Massaron, Alberto Boschetti |
| Autore | Massaron Luca |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, , 2016 |
| Descrizione fisica | 1 online resource (312 p.) |
| Collana | Community experience distilled |
| Soggetto topico |
Python (Computer program language)
Regression analysis |
| ISBN | 1-78398-074-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Regression - The Workhorse of Data Science; Regression analysis and data science; Exploring the promise of data science; The challenge; The linear models; What you are going to find in the book ; Python for data science; Installing Python; Choosing between Python 2 and Python 3; Step-by-step installation; Installing packages; Package upgrades; Scientific distributions; Introducing Jupyter or IPython; Python packages and functions for linear models ; NumPy; SciPy
StatsmodelsScikit-learn; Summary; Chapter 2: Approaching Simple Linear Regression; Defining a regression problem; Linear models and supervised learning; Reflecting on predictive variables; Reflecting on response variables; The family of linear models; Preparing to discover simple linear regression; Starting from the basics; A measure of linear relationship; Extending to linear regression; Regressing with StatsModels; The coefficient of determination; Meaning and significance of coefficients; Evaluating the fitted values; Correlation is not causation; Predicting with a regression model Regressing with Scikit-learnMinimizing the cost function; Explaining the reason for using squared errors; Pseudoinverse and other optimization methods; Gradient Descent at work; Summary; Chapter 3: Multiple Regression in Action; Using multiple features; Model building with Statsmodels; Using formulas as an alternative; The correlation matrix; Revisiting gradient descent; Feature scaling; Unstandardizing coefficients; Estimating feature importance; Inspecting standardized coefficients; Comparing models by R-squared; Interaction models; Discovering interactions; Polynomial regression Testing linear versus cubic transformationGoing for higher-degree solutions; Introducing underfitting and overfitting; Summary; Chapter 4: Logistic Regression; Defining a classification problem; Formalization of the problem: binary classification; Assessing the classifier's performance; Defining a probability-based approach; More on the logistic and logit functions; Let's see some code; Pros and cons of logistic regression; Revisiting Gradient Descend; Multiclass Logistic Regression; An example; Summary; Chapter 5: Data Preparation; Numeric feature scaling; Mean centering; Standardization NormalizationThe logistic regression case; Qualitative feature encoding; Dummy coding with Pandas; DictVectorizer and one-hot encoding; Feature hasher; Numeric feature transformation; Observing residuals; Summarizations by binning; Missing data; Missing data imputation; Keeping track of missing values; Outliers; Outliers on the response; Outliers among the predictors; Removing or replacing outliers; Summary; Chapter 6: Achieving Generalization; Checking on out-of-sample data; Testing by sample split; Cross-validation; Bootstrapping; Greedy selection of features ; The Madelon dataset Univariate selection of features |
| Record Nr. | UNINA-9910798272303321 |
|
Massaron Luca
|
||
| Birmingham : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Regression analysis with Python : learn the art of regression analysis with Python / / Luca Massaron, Alberto Boschetti
| Regression analysis with Python : learn the art of regression analysis with Python / / Luca Massaron, Alberto Boschetti |
| Autore | Massaron Luca |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, , 2016 |
| Descrizione fisica | 1 online resource (312 p.) |
| Collana | Community experience distilled |
| Soggetto topico |
Python (Computer program language)
Regression analysis |
| ISBN | 1-78398-074-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Regression - The Workhorse of Data Science; Regression analysis and data science; Exploring the promise of data science; The challenge; The linear models; What you are going to find in the book ; Python for data science; Installing Python; Choosing between Python 2 and Python 3; Step-by-step installation; Installing packages; Package upgrades; Scientific distributions; Introducing Jupyter or IPython; Python packages and functions for linear models ; NumPy; SciPy
StatsmodelsScikit-learn; Summary; Chapter 2: Approaching Simple Linear Regression; Defining a regression problem; Linear models and supervised learning; Reflecting on predictive variables; Reflecting on response variables; The family of linear models; Preparing to discover simple linear regression; Starting from the basics; A measure of linear relationship; Extending to linear regression; Regressing with StatsModels; The coefficient of determination; Meaning and significance of coefficients; Evaluating the fitted values; Correlation is not causation; Predicting with a regression model Regressing with Scikit-learnMinimizing the cost function; Explaining the reason for using squared errors; Pseudoinverse and other optimization methods; Gradient Descent at work; Summary; Chapter 3: Multiple Regression in Action; Using multiple features; Model building with Statsmodels; Using formulas as an alternative; The correlation matrix; Revisiting gradient descent; Feature scaling; Unstandardizing coefficients; Estimating feature importance; Inspecting standardized coefficients; Comparing models by R-squared; Interaction models; Discovering interactions; Polynomial regression Testing linear versus cubic transformationGoing for higher-degree solutions; Introducing underfitting and overfitting; Summary; Chapter 4: Logistic Regression; Defining a classification problem; Formalization of the problem: binary classification; Assessing the classifier's performance; Defining a probability-based approach; More on the logistic and logit functions; Let's see some code; Pros and cons of logistic regression; Revisiting Gradient Descend; Multiclass Logistic Regression; An example; Summary; Chapter 5: Data Preparation; Numeric feature scaling; Mean centering; Standardization NormalizationThe logistic regression case; Qualitative feature encoding; Dummy coding with Pandas; DictVectorizer and one-hot encoding; Feature hasher; Numeric feature transformation; Observing residuals; Summarizations by binning; Missing data; Missing data imputation; Keeping track of missing values; Outliers; Outliers on the response; Outliers among the predictors; Removing or replacing outliers; Summary; Chapter 6: Achieving Generalization; Checking on out-of-sample data; Testing by sample split; Cross-validation; Bootstrapping; Greedy selection of features ; The Madelon dataset Univariate selection of features |
| Record Nr. | UNINA-9910823184703321 |
|
Massaron Luca
|
||
| Birmingham : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
TensorFlow Deep Learning Projects [[electronic resource] /] / Massaron, Luca
| TensorFlow Deep Learning Projects [[electronic resource] /] / Massaron, Luca |
| Autore | Massaron Luca |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Packt Publishing, , 2018 |
| Descrizione fisica | 1 online resource (320 pages) |
| Soggetto genere / forma | Electronic books. |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910467483203321 |
|
Massaron Luca
|
||
| Packt Publishing, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||