YModel Tutorial

CONTENTS

Chapter 1 Basic Concepts

Chapter 2 Automatic Data Analysis

  • 2.1 Data preparation and import
  • 2.2 Identify data type
  • 2.3 Quantitative data analysis
  • 2.4 Qualitative data analysis
  • 2.5 Correlation analysis

Chapter 3 Data Preprocessing

  • 3.1 Fully automated preprocessing
  • 3.2 Introduction to preprocessing methods
    • Variable preliminary filtering
    • Outlier handling
    • Missing value handling
    • Categorical variable handling
    • Time variable processing
    • Skewness handling
    • Balanced sampling
    • Data standardization
    • Dataset split

Chapter 4 Modeling

  • 4.1 Supervised learning
  • 4.2 One-click modeling
  • 4.3 Algorithm description
    • Linear model
    • Tree model
    • Ensemble learning
    • Deep learning

Chapter 5 Model evaluation

  • 5.1 Classification model evaluation
    • Confusion matrix
    • Accuracy table
    • ROC, AUC
    • Gini, KS
    • Lift graph
    • Recall chart
  • 5.2 Regression model evaluation
    • Model error evaluation
    • Residual plot
    • Result comparison graph

Chapter 6 Model tuning

  • 6.1 Derived variable
    • Binning
    • Feature variable self-transformation
    • Variable transformation align with target
    • Variable interaction
    • Ratio
    • Date time variable
    • Other derivatives
  • 6.2 Algorithm selection and parameter tuning
    • Algorithm selection
    • Parameter tuning
  • 6.3 Appendix - Common algorithm parameters

Chapter 7 Comprehensive cases

  • 7.1 Classification model case
  • 7.2 Regression model case

Resource