Interpretable Machine Learning Applications: Part 2

Instructor: Epaminondas Kapetanios

What you'll learn

  •   Apply Local Interpretable Model-agnostic Explanations (LIME) as a machine learning interpretation
  •   Explain individual predictions being made by a trained machine learning model.
  •   Add aspects for individual predictions in your Machine Learning applications.
  • Skills you'll practice

  •   Regression Analysis
  •   Predictive Modeling
  •   Machine Learning
  •   Classification And Regression Tree (CART)
  •   Feature Engineering
  •   Data Processing
  •   Exploratory Data Analysis
  •   Machine Learning Algorithms
  •   Random Forest Algorithm
  •   Applied Machine Learning
  •   Performance Measurement
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