Human Interpretable Machine Learning — The Need and Importance of Model Interpretation (with hands-on examples)
The field of Machine Learning has gone through some phenomenal changes over the last decade. In the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.
A machine learning model by itself consists of an algorithm which tries to learn latent patterns and relationships from data without hard-coding fixed rules. Hence, explaining how a model works to the business always poses its own set of challenges. In this talk, I will be covering the need and importance of human interpretable machine learning approaches, look at effective strategies for model interpretation and several hands-on examples. Detailed coverage of open-source frameworks for machine learning model interpretation will also be one of the major focus areas. Examples will be showcased in Python.
Outline/Structure of the Talk
Part 1: The Need and Importance of Model Interpretation
- Understanding Model Interpretation
- Importance of Model Interpretation
- Criteria for Model Interpretation Methods
- Scope of Model Interpretation
Part 2: Model Interpretation Techniques
- Model Interpretation Strategies
- Existing Techniques for Model Interpretation
- Challenges and Limitations of Existing Techniques
- Strategies for combating the accuracy vs. interpretability trade-off
Part 3: Hands-on Model Interpretation
- Introducing and Understanding Skater
- Hands-on Machine Learning Model Interpretation with Skater
- Model interpretation for regression and classification problems
Part 4: Hands-on Advanced Model Interpretation
- Hands-on Model Interpretation on Unstructured Data (text) if time permits.
- Understand the need of model interpretation in the real-world
- Learn about present gaps between data science and business stakeholders
- Learn about present techniques and limitations around model evaluation
- Deep dive into effective model interpretation strategies
- Learn about popular open-source model interpretation frameworks
- Get a detailed perspective on how model interpretation is used with hands-on examples
Data Scientists, Data Enthusiasts, Data Analysts, Managers & Anyone with an interest in Machine Learning
Prerequisites for Attendees
Basic concepts around machine learning like models, performance evaluation techniques would help. However we will be covering them during the talk so it is not compulsory.
schedule Submitted 3 years ago
People who liked this proposal, also liked:
Favio Vázquez - Agile Data Science Workflows with Python, Spark and OptimusFavio VázquezSr. Data ScientistRaken Data Group
schedule 3 years agoSold Out!
Cleaning, Preparing , Transforming and Exploring Data is the most time-consuming and least enjoyable data science task, but one of the most important ones. With Optimus we’ve solve this problem for small or huge datasets, also improving a whole workflow for data science, making it easier for everyone. You will learn how the combination of Apache Spark and Optimus with the Python ecosystem can form a whole framework for Agile Data Science allowing people and companies to go further, and beyond their common sense and intuition to solve complex business problems.
Dipanjan Sarkar - The Art of Effective Visualization of Multi-dimensional Data - A hands-on ApproachDipanjan SarkarData Science LeadApplied Materials
schedule 3 years agoSold Out!
Descriptive Analytics is one of the core components of any analysis life-cycle pertaining to a data science project or even specific research. Data aggregation, summarization and visualization are some of the main pillars supporting this area of data analysis. However, dealing with multi-dimensional datasets with typically more than two attributes start causing problems, since our medium of data analysis and communication is typically restricted to two dimensions. We will explore some effective strategies of visualizing data in multiple dimensions (ranging from 1-D up to 6-D) using a hands-on approach with Python and popular open-source visualization libraries like matplotlib and seaborn. We will also do a brief coverage on excellent R visualization libraries like ggplot if we have time.
BONUS: We will also look at ways to visualize unstructured data with several dimensions including text, images and audio!
Jared Lander - Machine Learning with RJared LanderChief Data ScientistLander Analytics
schedule 2 years agoSold Out!
Modern statistics has become almost synonymous with machine learning - a collection of techniques that utilize today's incredible computing power. Jared Lander walks you through the available methods for implementing machine learning algorithms in R and explores underlying theories such as the elastic net and boosted trees.
- Building the design matrix
- Penalized regression with the lasso and ridge methods
- Fitting models with glmnet
- Interactive visualization of the coefficient path
- Use cross-validation to choose the optimal lambda
- Visualize coefficients with coefplot
- Perform binary classification with a single tree with xgboost
- Train a boosted tree
- Tune xgboost hyperparameters
- Use validation data to understand performance
- Visualize variable importance
- Train a boosted random forest with xgboost
Dipanjan Sarkar - Unleash the Power of Deep Learning with Transfer LearningDipanjan SarkarData Science LeadApplied Materials
schedule 3 years agoSold Out!
Transfer learning is a machine learning \ deep learning technique where knowledge gained during training in one set of machine learning problem can be used to train other similar types of problems. This is an extremely useful approach to leveraging pre-trained models to solve real-world problems having constraints and limitations of less data availability.
This talk will cover essentials around deep learning and transfer learning concepts. The various methodologies of transfer learning. We will then look at diverse ways of how transfer learning can be applied in the real-world on complex problems around the following areas.
- Computer Vision
- Natural Language Processing
- Audio Categorization
We will briefly look at a multitude of real-world case studies and problems around the preceding areas like text classification, image classification, image captioning, style transfer and audio classification.