We will continue from where we left off in the previous section, on analyzing and treating missing values. Data scientists spend the majority of their time doing data preparation and exploration, not model building and optimization. Now that our dataset has no missing values, we can proceed with our exploratory data analysis.
Exploratory data analysis
by Vijayalakshmi Natarajan, Dipayan Sarkar
Ensemble Machine Learning Cookbook
- Title Page
- Copyright and Credits
- About Packt
- Foreword
- Contributors
- Preface
- Get Closer to Your Data
- Getting Started with Ensemble Machine Learning
- Resampling Methods
- Statistical and Machine Learning Algorithms
- Bag the Models with Bagging
- When in Doubt, Use Random Forests
- Boosting Model Performance with Boosting
- Introduction to boosting
- Implementing AdaBoost for disease risk prediction using scikit-learn
- Implementing a gradient boosting machine for disease risk prediction using scikit-learn
- Implementing the extreme gradient boosting method for glass identification using XGBoost with scikit-learn 
- Blend It with Stacking
- Homogeneous Ensembles Using Keras
- Heterogeneous Ensemble Classifiers Using H2O
- Heterogeneous Ensemble for Text Classification Using NLP
- Homogenous Ensemble for Multiclass Classification Using Keras
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Exploratory data analysis
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