Mastering Predictive Analytics with Scikit-Learn and TensorFlow :: Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python.
In this book, you will find a range of methods to improve the performance of almost any predictive model, from ensemble methods to dimensionality reduction and cross-validation. You will learn the tools to produce advanced predictive models. In addition, you will dive into the exiting field of Deep...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing Ltd,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | In this book, you will find a range of methods to improve the performance of almost any predictive model, from ensemble methods to dimensionality reduction and cross-validation. You will learn the tools to produce advanced predictive models. In addition, you will dive into the exiting field of Deep Learning using TensorFlow. |
| Beschreibung: | Evaluating the model with a set threshold. |
| Beschreibung: | 1 online resource (149 pages) |
| ISBN: | 9781789612240 1789612241 |
Internformat
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| 245 | 1 | 0 | |a Mastering Predictive Analytics with Scikit-Learn and TensorFlow : |b Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. |
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Packt Upsell; Contributor; Table of Contents; Preface; Chapter 1: Ensemble Methods for Regression and Classification; Ensemble methods and their working; Bootstrap sampling; Bagging; Random forests; Boosting; Ensemble methods for regression; The diamond dataset; Training different regression models; KNN model; Bagging model; Random forests model; Boosting model; Using ensemble methods for classification; Predicting a credit card dataset ; Training different regression models; Logistic regression model; Bagging model; Random forest model. | |
| 505 | 8 | |a Boosting modelSummary; Chapter 2: Cross-validation and Parameter Tuning; Holdout cross-validation; K-fold cross-validation; Implementing k-fold cross-validation; Comparing models with k-fold cross-validation; Introduction to hyperparameter tuning; Exhaustive grid search; Hyperparameter tuning in scikit-learn; Comparing tuned and untuned models; Summary; Chapter 3: Working with Features; Feature selection methods ; Removing dummy features with low variance; Identifying important features statistically; Recursive feature elimination; Dimensionality reduction and PCA; Feature engineering. | |
| 505 | 8 | |a Creating new featuresImproving models with feature engineering; Training your model; Reducible and irreducible error; Summary; Chapter 4: Introduction to Artificial Neural Networks and TensorFlow; Introduction to ANNs; Perceptrons; Multilayer perceptron; Elements of a deep neural network model; Deep learning; Elements of an MLP model; Introduction to TensorFlow; TensorFlow installation; Core concepts in TensorFlow; Tensors; Computational graph; Summary; Chapter 5: Predictive Analytics with TensorFlow and Deep Neural Networks; Predictions with TensorFlow; Introduction to the MNIST dataset. | |
| 505 | 8 | |a Building classification models using MNIST datasetElements of the DNN model; Building the DNN; Reading the data; Defining the architecture; Placeholders for inputs and labels; Building the neural network; The loss function; Defining optimizer and training operations; Training strategy and valuation of accuracy of the classification; Running the computational graph; Regression with Deep Neural Networks (DNN); Elements of the DNN model; Building the DNN; Reading the data; Objects for modeling; Training strategy; Input pipeline for the DNN; Defining the architecture. | |
| 505 | 8 | |a Placeholders for input values and labelsBuilding the DNN; The loss function; Defining optimizer and training operations; Running the computational graph; Classification with DNNs; Exponential linear unit activation function; Classification with DNNs; Elements of the DNN model; Building the DNN; Reading the data; Producing the objects for modeling; Training strategy; Input pipeline for DNN; Defining the architecture; Placeholders for inputs and labels; Building the neural network; The loss function; Evaluation nodes; Optimizer and the training operation; Run the computational graph. | |
| 500 | |a Evaluating the model with a set threshold. | ||
| 520 | |a In this book, you will find a range of methods to improve the performance of almost any predictive model, from ensemble methods to dimensionality reduction and cross-validation. You will learn the tools to produce advanced predictive models. In addition, you will dive into the exiting field of Deep Learning using TensorFlow. | ||
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| 650 | 0 | |a Decision making |x Data processing. | |
| 650 | 0 | |a Application software |x Development. |0 http://id.loc.gov/authorities/subjects/sh95009362 | |
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
| 650 | 2 | |a Data Mining |0 https://id.nlm.nih.gov/mesh/D057225 | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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| contents | Cover; Title Page; Copyright and Credits; Packt Upsell; Contributor; Table of Contents; Preface; Chapter 1: Ensemble Methods for Regression and Classification; Ensemble methods and their working; Bootstrap sampling; Bagging; Random forests; Boosting; Ensemble methods for regression; The diamond dataset; Training different regression models; KNN model; Bagging model; Random forests model; Boosting model; Using ensemble methods for classification; Predicting a credit card dataset ; Training different regression models; Logistic regression model; Bagging model; Random forest model. Boosting modelSummary; Chapter 2: Cross-validation and Parameter Tuning; Holdout cross-validation; K-fold cross-validation; Implementing k-fold cross-validation; Comparing models with k-fold cross-validation; Introduction to hyperparameter tuning; Exhaustive grid search; Hyperparameter tuning in scikit-learn; Comparing tuned and untuned models; Summary; Chapter 3: Working with Features; Feature selection methods ; Removing dummy features with low variance; Identifying important features statistically; Recursive feature elimination; Dimensionality reduction and PCA; Feature engineering. Creating new featuresImproving models with feature engineering; Training your model; Reducible and irreducible error; Summary; Chapter 4: Introduction to Artificial Neural Networks and TensorFlow; Introduction to ANNs; Perceptrons; Multilayer perceptron; Elements of a deep neural network model; Deep learning; Elements of an MLP model; Introduction to TensorFlow; TensorFlow installation; Core concepts in TensorFlow; Tensors; Computational graph; Summary; Chapter 5: Predictive Analytics with TensorFlow and Deep Neural Networks; Predictions with TensorFlow; Introduction to the MNIST dataset. Building classification models using MNIST datasetElements of the DNN model; Building the DNN; Reading the data; Defining the architecture; Placeholders for inputs and labels; Building the neural network; The loss function; Defining optimizer and training operations; Training strategy and valuation of accuracy of the classification; Running the computational graph; Regression with Deep Neural Networks (DNN); Elements of the DNN model; Building the DNN; Reading the data; Objects for modeling; Training strategy; Input pipeline for the DNN; Defining the architecture. Placeholders for input values and labelsBuilding the DNN; The loss function; Defining optimizer and training operations; Running the computational graph; Classification with DNNs; Exponential linear unit activation function; Classification with DNNs; Elements of the DNN model; Building the DNN; Reading the data; Producing the objects for modeling; Training strategy; Input pipeline for DNN; Defining the architecture; Placeholders for inputs and labels; Building the neural network; The loss function; Evaluation nodes; Optimizer and the training operation; Run the computational graph. |
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| discipline | Informatik |
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| publisher | Packt Publishing Ltd, |
| record_format | marc |
| spelling | Fuentes, Alvaro. Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. Birmingham : Packt Publishing Ltd, 2018. 1 online resource (149 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Title Page; Copyright and Credits; Packt Upsell; Contributor; Table of Contents; Preface; Chapter 1: Ensemble Methods for Regression and Classification; Ensemble methods and their working; Bootstrap sampling; Bagging; Random forests; Boosting; Ensemble methods for regression; The diamond dataset; Training different regression models; KNN model; Bagging model; Random forests model; Boosting model; Using ensemble methods for classification; Predicting a credit card dataset ; Training different regression models; Logistic regression model; Bagging model; Random forest model. Boosting modelSummary; Chapter 2: Cross-validation and Parameter Tuning; Holdout cross-validation; K-fold cross-validation; Implementing k-fold cross-validation; Comparing models with k-fold cross-validation; Introduction to hyperparameter tuning; Exhaustive grid search; Hyperparameter tuning in scikit-learn; Comparing tuned and untuned models; Summary; Chapter 3: Working with Features; Feature selection methods ; Removing dummy features with low variance; Identifying important features statistically; Recursive feature elimination; Dimensionality reduction and PCA; Feature engineering. Creating new featuresImproving models with feature engineering; Training your model; Reducible and irreducible error; Summary; Chapter 4: Introduction to Artificial Neural Networks and TensorFlow; Introduction to ANNs; Perceptrons; Multilayer perceptron; Elements of a deep neural network model; Deep learning; Elements of an MLP model; Introduction to TensorFlow; TensorFlow installation; Core concepts in TensorFlow; Tensors; Computational graph; Summary; Chapter 5: Predictive Analytics with TensorFlow and Deep Neural Networks; Predictions with TensorFlow; Introduction to the MNIST dataset. Building classification models using MNIST datasetElements of the DNN model; Building the DNN; Reading the data; Defining the architecture; Placeholders for inputs and labels; Building the neural network; The loss function; Defining optimizer and training operations; Training strategy and valuation of accuracy of the classification; Running the computational graph; Regression with Deep Neural Networks (DNN); Elements of the DNN model; Building the DNN; Reading the data; Objects for modeling; Training strategy; Input pipeline for the DNN; Defining the architecture. Placeholders for input values and labelsBuilding the DNN; The loss function; Defining optimizer and training operations; Running the computational graph; Classification with DNNs; Exponential linear unit activation function; Classification with DNNs; Elements of the DNN model; Building the DNN; Reading the data; Producing the objects for modeling; Training strategy; Input pipeline for DNN; Defining the architecture; Placeholders for inputs and labels; Building the neural network; The loss function; Evaluation nodes; Optimizer and the training operation; Run the computational graph. Evaluating the model with a set threshold. In this book, you will find a range of methods to improve the performance of almost any predictive model, from ensemble methods to dimensionality reduction and cross-validation. You will learn the tools to produce advanced predictive models. In addition, you will dive into the exiting field of Deep Learning using TensorFlow. Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Decision making Data processing. Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) Données volumineuses. Prise de décision Informatique. Logiciels d'application Développement. Python (Langage de programmation) Information theory. bicssc Computer modelling & simulation. bicssc Natural language & machine translation. bicssc Information architecture. bicssc Computers Natural Language Processing. bisacsh Computers Computer Simulation. bisacsh Computers Information Theory. bisacsh Application software Development fast Big data fast Data mining fast Decision making Data processing fast Python (Computer program language) fast Print version: Fuentes, Alvaro. Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. Birmingham : Packt Publishing Ltd, ©2018 9781789617740 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1905995 Volltext |
| spellingShingle | Fuentes, Alvaro Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. Cover; Title Page; Copyright and Credits; Packt Upsell; Contributor; Table of Contents; Preface; Chapter 1: Ensemble Methods for Regression and Classification; Ensemble methods and their working; Bootstrap sampling; Bagging; Random forests; Boosting; Ensemble methods for regression; The diamond dataset; Training different regression models; KNN model; Bagging model; Random forests model; Boosting model; Using ensemble methods for classification; Predicting a credit card dataset ; Training different regression models; Logistic regression model; Bagging model; Random forest model. Boosting modelSummary; Chapter 2: Cross-validation and Parameter Tuning; Holdout cross-validation; K-fold cross-validation; Implementing k-fold cross-validation; Comparing models with k-fold cross-validation; Introduction to hyperparameter tuning; Exhaustive grid search; Hyperparameter tuning in scikit-learn; Comparing tuned and untuned models; Summary; Chapter 3: Working with Features; Feature selection methods ; Removing dummy features with low variance; Identifying important features statistically; Recursive feature elimination; Dimensionality reduction and PCA; Feature engineering. Creating new featuresImproving models with feature engineering; Training your model; Reducible and irreducible error; Summary; Chapter 4: Introduction to Artificial Neural Networks and TensorFlow; Introduction to ANNs; Perceptrons; Multilayer perceptron; Elements of a deep neural network model; Deep learning; Elements of an MLP model; Introduction to TensorFlow; TensorFlow installation; Core concepts in TensorFlow; Tensors; Computational graph; Summary; Chapter 5: Predictive Analytics with TensorFlow and Deep Neural Networks; Predictions with TensorFlow; Introduction to the MNIST dataset. Building classification models using MNIST datasetElements of the DNN model; Building the DNN; Reading the data; Defining the architecture; Placeholders for inputs and labels; Building the neural network; The loss function; Defining optimizer and training operations; Training strategy and valuation of accuracy of the classification; Running the computational graph; Regression with Deep Neural Networks (DNN); Elements of the DNN model; Building the DNN; Reading the data; Objects for modeling; Training strategy; Input pipeline for the DNN; Defining the architecture. Placeholders for input values and labelsBuilding the DNN; The loss function; Defining optimizer and training operations; Running the computational graph; Classification with DNNs; Exponential linear unit activation function; Classification with DNNs; Elements of the DNN model; Building the DNN; Reading the data; Producing the objects for modeling; Training strategy; Input pipeline for DNN; Defining the architecture; Placeholders for inputs and labels; Building the neural network; The loss function; Evaluation nodes; Optimizer and the training operation; Run the computational graph. Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Decision making Data processing. Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) Données volumineuses. Prise de décision Informatique. Logiciels d'application Développement. Python (Langage de programmation) Information theory. bicssc Computer modelling & simulation. bicssc Natural language & machine translation. bicssc Information architecture. bicssc Computers Natural Language Processing. bisacsh Computers Computer Simulation. bisacsh Computers Information Theory. bisacsh Application software Development fast Big data fast Data mining fast Decision making Data processing fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh2012003227 http://id.loc.gov/authorities/subjects/sh95009362 http://id.loc.gov/authorities/subjects/sh96008834 https://id.nlm.nih.gov/mesh/D057225 |
| title | Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. |
| title_auth | Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. |
| title_exact_search | Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. |
| title_full | Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. |
| title_fullStr | Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. |
| title_full_unstemmed | Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. |
| title_short | Mastering Predictive Analytics with Scikit-Learn and TensorFlow : |
| title_sort | mastering predictive analytics with scikit learn and tensorflow implement machine learning techniques to build advanced predictive models using python |
| title_sub | Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. |
| topic | Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Decision making Data processing. Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) Données volumineuses. Prise de décision Informatique. Logiciels d'application Développement. Python (Langage de programmation) Information theory. bicssc Computer modelling & simulation. bicssc Natural language & machine translation. bicssc Information architecture. bicssc Computers Natural Language Processing. bisacsh Computers Computer Simulation. bisacsh Computers Information Theory. bisacsh Application software Development fast Big data fast Data mining fast Decision making Data processing fast Python (Computer program language) fast |
| topic_facet | Data mining. Big data. Decision making Data processing. Application software Development. Python (Computer program language) Data Mining Exploration de données (Informatique) Données volumineuses. Prise de décision Informatique. Logiciels d'application Développement. Python (Langage de programmation) Information theory. Computer modelling & simulation. Natural language & machine translation. Information architecture. Computers Natural Language Processing. Computers Computer Simulation. Computers Information Theory. Application software Development Big data Data mining Decision making Data processing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1905995 |
| work_keys_str_mv | AT fuentesalvaro masteringpredictiveanalyticswithscikitlearnandtensorflowimplementmachinelearningtechniquestobuildadvancedpredictivemodelsusingpython |