Data Science in H₂O

• Generalized Linear Model (GLM)
  • Defining a GLM Model
  • Expert Settings
  • Interpreting a Model
  • Expert Settings
  • Validate GLM
  • Cross Validation
  • Cost of Computation
  • GLM Algorithm
  • References
• K-Means
  • When to use K-Means
  • Defining a K-Means model
  • Interpreting a Model
  • References
  • K-Means Algorithm
  • References
• Random Forest (RF)
  • When to use RF
  • Defining a Model
  • Interpreting Results
  • RF Error Rates
• Random Forest Data Science
• Principal Components Analysis
  • Defining a PCA Model
  • Interpreting Results
  • Notes on the application of PCA
• Summary
  • Inputs
  • Output
• Gradient Boosted Regression and Classification
  • Defining a GBM Model
  • Interpreting Results
  • GBM Algorithm
  • Reference
• Deep Learning
  • Defining a Deep Learning Model
  • Interpreting the Model
• Data Science and Machine Learning
• SGD
  • References
• References
  • Recommended Reading
  • GLM
  • Poisson
  • Logistic (binomial and multinomial)
  • GBM
  • Neural Networks
  • Tweedie
  • K-Means