MACHINE LEARNING: Neural Networks, Decision Trees and Support Vector Machine with IBM SPSS Modeler
- Length: 335 pages
- Edition: 1
- Language: English
- Publisher: lulu.com
- Publication Date: 2021-12-26
- ISBN-10: B09P9V78M4
- Sales Rank: #0 (See Top 100 Books)
Machine Learning techniques are intended to extract the knowledge contained in the data through models and other appropriate techniques. Within Machine Learning techniques there are two fundamental types: supervised learning techniques and unsupervised learning techniques. Supervised learning techniques include all those that use a model in which there are dependent variables and independent variables. The purpose of these techniques is usually prediction or classification of both at the same time. Neural networks, decision trees, and SVM models are supervised learning machine learning techniques for prediction and classification. It is precisely these techniques that are developed in this book using IBM SPSS Modeler software.
INTRODUCCTION TO IBM SPSS MODELER NEURAL NETWORKS Functionality of one neuron and the activation function The topology and layers of an Neural Network Necessary conditions and further remarks What Applications Should Neural Networks Be Used For? NEURAL NETWORKS STEP TO STEP. A COMPLETE EXAMPLE Building the Stream Browsing the Model BAYESIAN NEURAL NETWORK algorithms AND EXAMPLES. Bayesian Networks Algorithms Primary Calculations Handling of Continuous Predictors Feature Selection via Breadth-First Search Tree Augmented Naïve Bayes Method Markov Blanket Algorithms Model Nugget/Scoring EXAMPLE WITH BAYESIAN NETWORK MODEL Evaluating the Model NEURAL NETWORKS ALGORITHMS AND EXAMPLES MULTILAYER PERCEPTRON Radial Basis Function 3.1 NEURAL NETWORKS WITH IBM SPSS. A COMPLETE EXAMPLE 3.1.1 Training of an NN 3.1.2 Validation of the NN 3.1.3 The Model Nugget 3.2 Neural network comparison with other models 3.3 Finding the best network topology NEURAL NETWORK AND Decision TREE ALGORITHMS 4.1 Neural Net/C&RT EXAMPLE 4.1.1 Examining the Data 4.1.2 Learning and Testing 4.2 Neural Net/C5.0 example 4.2.1 Examining the Data 4.2.2 Data Preparation 4.2.3 Learning 4.2.4 Testing 4.3 BUILDING DECISION TREES USING C&R Node 4.4 BUILDING DECISION TREES USING THE QUEST NODE 4.5 Building a Decision Tree with the C5.0 Node 4.5.1 The Model Nugget 4.6 Building a Decision Tree with the CHAID Node 4.6.1 Building the Stream 4.6.2 Browsing the Model 4.6.3 Evaluating the Model 4.6.4 Scoring Records SUPPORT VECTOR MACHINE (SVM) ALGORITHMS 5.1 SUPPORT VECTOR MACHINE AND MACHINE LEARNIG 5.2 QUEST Algorithms 5.3 BUILDING DECISION TREES USING THE QUEST NODE NEURAL NETWORKS ALGORITHMS FOR CLASIFICATION 6.1 COMPARISON OF DECISION TREE NODES 6.2 Rule set and cross-validation with C5.0 AUTOMATIC PREDICTION ALGORITMS 7.1 Decision Tree Models 7.2 Tree-Building Algorithms 7.3 General Uses of Tree-Based Analysis 7.4 The Interactive Tree Builder 7.5 Growing and Pruning the Tree 7.6 Defining Custom Splits 7.7 Viewing Predictor Details 7.8 Split Details and Surrogates 7.9 Customizing the Tree View 7.10 Displaying Statistics and Graphs 7.11 Gains 7.12 Classification Gains 7.13 Classification Profits and ROI 7.14 Regression Gains 7.15 Gains Charts 7.16 Gains-Based Selection 7.17 Risks 7.18 Saving Tree Models and Results 7.19 Generating a Model from the Tree Builder 7.20 Tree-Growing Directives 7.21 Generating a Rule Set from a Decision Tree 7.22 Building a Tree Model Directly 7.23 Decision Tree Nodes 7.24 C&R Tree Node 7.25 CHAID Node 7.26 QUEST Node 7.27 Decision Tree Node Fields Options 7.28 Decision Tree Node Build Options 7.29 Decision Tree Nodes - Objectives 7.30 Decision Tree Nodes - Basics 7.31 Pruning (C&RT and QUEST only) 7.32 Decision Tree Nodes - Stopping Rules 7.33 Decision Tree Nodes - Ensembles 7.34 C&R Tree and QUEST Nodes - Costs & Priors 7.35 Priors 7.36 CHAID Node - Costs 7.37 C&R Tree Node - Advanced 7.38 QUEST Node - Advanced 7.39 CHAID Node - Advanced 7.40 Decision Tree Node Model Options 7.41 C5.0 Node 7.42 C5.0 Node Model Options 7.43 Tree-AS node 7.44 Random Trees node 7.45 C&R Tree, CHAID, QUEST, and C5.0 decision tree model nuggets NEURAL NETWORKS WITH IBM SPSS MODELER
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.