Offered By: IBMSkillsNetwork
Med Prediction with Explainable AI: Partial Dependence Plot
Dive into machine learning with Python, scikit-learn, and Explainable AI (XAI) techniques like Partial Dependence Plots. Imagine a machine learning model predicts your likelihood of a heart attack—but you’re wondering why. Was it your age, cholesterol level, or a combination of factors? This lack of clarity is where Explainable AI (XAI) steps in, helping us understand how models make predictions. One powerful XAI tool is the Partial Dependence Plot (PDP). PDPs reveal how individual features like age or cholesterol influence a model's output.
Continue readingGuided Project
Data Science
249 EnrolledAt a Glance
Dive into machine learning with Python, scikit-learn, and Explainable AI (XAI) techniques like Partial Dependence Plots. Imagine a machine learning model predicts your likelihood of a heart attack—but you’re wondering why. Was it your age, cholesterol level, or a combination of factors? This lack of clarity is where Explainable AI (XAI) steps in, helping us understand how models make predictions. One powerful XAI tool is the Partial Dependence Plot (PDP). PDPs reveal how individual features like age or cholesterol influence a model's output.
Understanding machine learning and model interpretation can significantly enhance your analytical skill set, especially in the rapidly evolving field of healthcare. In this guided project, you will start on a journey to predict heart attack risks using Python, scikit-learn, and Explainable AI (XAI) techniques such as Partial Dependence Plots. By working with real-world health data, you'll not only build a predictive model but also gain insights into how features like age, cholesterol, and blood pressure impact the model's predictions. This project is not just about technical prowess; it's about developing the ability to make informed, data-driven decisions that can improve patient outcomes and communication. This 60-minute tutorial is your gateway to mastering the art of model interpretation and understanding feature interactions, empowering you to extract meaningful insights from data that can transform healthcare decision-making.
#What You'll Learn
After completing this project, you will know:
- - How to **interpret complex machine learning models**, particularly focusing on **Partial Dependence Plots (PDPs)**
- - How to analyze **feature interactions** and their impact on model predictions
- - How to use both **logistic regression** and **random forest** models on real healthcare data **(heart disease dataset)**
- - How to evaluate and compare different models using various metrics and visualization techniques
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.
Certificate
No Certificate Offered
Estimated Effort
1 Hour
Level
Advanced
Industries
Skills You Will Learn
Data Science, Explainable AI, Healthcare, Machine Learning, Pandas, sklearn
Language
English
Course Code
GPXX0OU0EN