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會員
Machine Learning for Data Mining
Machinelearning(ML)combinedwithdataminingcangiveyouamazingresultsinyourdataminingworkbyempoweringyouwithseveralwaystolookatdata.Thisbookwillhelpyouimproveyourdataminingtechniquesbyusingsmartmodelingtechniques.ThisbookwillteachyouhowtoimplementMLalgorithmsandtechniquesinyourdataminingwork.Itwillenableyoutopairthebestalgorithmswiththerighttoolsandprocesses.Youwilllearnhowtoidentifypatternsandmakepredictionswithminimalhumanintervention.YouwillbuilddifferenttypesofMLmodels,suchastheneuralnetwork,theSupportVectorMachines(SVMs),andtheDecisiontree.Youwillseehowallofthesemodelsworksandwhatkindofdatainthedatasettheyaresuitedfor.Youwilllearnhowtocombinetheresultsofdifferentmodelsinordertoimproveaccuracy.Topicssuchasremovingnoiseandhandlingerrorswillgiveyouanaddededgeinmodelbuildingandoptimization.Bytheendofthisbook,youwillbeabletobuildpredictivemodelsandextractinformationofinterestfromthedataset
目錄(89章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Machine Learning for Data Mining
- Contributors
- About the author
- Packt is searching for authors like you
- About Packt
- Why subscribe?
- Packt.com
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Introducing Machine Learning Predictive Models
- Characteristics of machine learning predictive models
- Types of machine learning predictive models
- Working with neural networks
- Advantages of neural networks
- Disadvantages of neural networks
- Representing the errors
- Types of neural network models
- Multi-layer perceptron
- Why are weights important?
- An example representation of a multilayer perceptron model
- The linear regression model
- A sample neural network model
- Feed-forward backpropagation
- Model training ethics
- Summary
- Getting Started with Machine Learning
- Demonstrating a neural network
- Running a neural network model
- Interpreting results
- Analyzing the accuracy of the model
- Model performance on testing partition
- Support Vector Machines
- Working with Support Vector Machines
- Kernel transformation
- But what is the best solution?
- Types of kernel functions
- Demonstrating SVMs
- Interpreting the results
- Trying additional solutions
- Summary
- Understanding Models
- Models
- Statistical models
- Decision tree models
- Machine learning models
- Using graphs to interpret machine learning models
- Using statistics to interpret machine learning models
- Understanding the relationship between a continuous predictor and a categorical outcome variable
- Using decision trees to interpret machine learning models
- Summary
- Improving Individual Models
- Modifying model options
- Using a different model to improve results
- Removing noise to improve models
- How to remove noise
- Doing additional data preparation
- Preparing the data
- Balancing data
- The need for balancing data
- Implementing balance in data
- Summary
- Advanced Ways of Improving Models
- Combining models
- Combining by voting
- Combining by highest confidence
- Implementing combining models
- Combining models in Modeler
- Combining models outside Modeler
- Using propensity scores
- Implementations of propensity scores
- Meta-level modeling
- Error modeling
- Boosting and bagging
- Boosting
- Bagging
- Predicting continuous outcomes
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-24 14:50:49
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