Offered By: IBMSkillsNetwork
Med Prediction with Explainable AI: Partial Dependence Plot
Learn to interpret machine learning models by visualizing feature impacts using Python, scikit-learn, and Explainable AI (XAI) techniques like Partial Dependence Plots (PDPs). These visualizations show relationships between features like age, income, or medical metrics and model predictions, translating complex algorithms into clear insights. Data scientists and stakeholders can use PDPs to understand model behaviour. This interpretability technique supports technical analysis and business decision-making.
Continue readingGuided Project
Data Science
At a Glance
Learn to interpret machine learning models by visualizing feature impacts using Python, scikit-learn, and Explainable AI (XAI) techniques like Partial Dependence Plots (PDPs). These visualizations show relationships between features like age, income, or medical metrics and model predictions, translating complex algorithms into clear insights. Data scientists and stakeholders can use PDPs to understand model behaviour. This interpretability technique supports technical analysis and business decision-making.
Understanding how machine learning models make predictions is a critical skill, especially in high-stakes domains like healthcare. In this hands-on guided project, you’ll predict heart attack risks using Python, scikit-learn, and Explainable AI (XAI) techniques like Partial Dependence Plots (PDPs). By working with a real-world heart disease dataset, you’ll not only create predictive models but also learn to interpret their outputs, uncovering how features such as age, cholesterol levels, and blood pressure influence predictions. Beyond technical skills, this project emphasizes using data to make informed decisions, empowering you to communicate findings that can positively impact patient care. In just 60 minutes, you'll gain a practical understanding of PDPs and their role in explaining machine learning models, equipping you to navigate the intersection of AI and healthcare.
What You'll Learn
By the end of this project, you will have mastered:
- - Interpreting machine learning models using Partial Dependence Plots (PDPs) to visualize feature impact.
- - Understanding and analyzing feature interactions within models.
- - Applying and comparing logistic regression and random forest models on healthcare data.
- - Using metrics and visualization tools to evaluate model performance and interpretability effectively.
What You'll Need
To get started with this guided project, you should have:
- - A basic understanding of Python programming.
- - Familiarity with fundamental machine learning concepts.
- - Access to modern web browsers like Chrome, Edge, Firefox, Internet Explorer, or Safari.
Ready to demystify machine learning models? Start this guided project now and unlock the ability to interpret complex algorithms, transforming raw data into actionable healthcare insights.
Estimated Effort
1 Hour
Level
Advanced
Skills You Will Learn
Data Science, Explainable AI, Healthcare, Machine Learning, Pandas, sklearn
Language
English
Course Code
GPXX0OU0EN