8 Characteristics of a Good Machine Learning Feature | Predictive, Variety, Interpretability, Ethics

8 Characteristics of a Good Machine Learning Feature | Predictive, Variety, Interpretability, Ethics

Share:

Similar Tracks

Feature Selection using Hierarchical Clustering | Python Tutorial A Data Odyssey

Interpretable Feature Engineering | How to Build Intuitive Machine Learning Features A Data Odyssey

11. Introduction to Machine Learning MIT OpenCourseWare

8 Plots for Explaining Linear Regression | Residuals, Weight, Effect & SHAP A Data Odyssey

EMPATHS-1: Kick-off intro session Gjalt-Jorn Peters

Think Fast, Talk Smart: Communication Techniques Stanford Graduate School of Business

Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math A Data Odyssey

AI Ethics and Fairness by Benedetta Giovanola (AI Ethics: Global Perspectives) The GovLab

Permutation Feature Importance from Scratch | Explanation & Python Code A Data Odyssey

Machine Learning Tutorial | Machine Learning Basics | Machine Learning Algorithms | Simplilearn Simplilearn

Explaining Anomalies with Isolation Forest and SHAP | Python Tutorial A Data Odyssey

Modelling Non-linear Relationships with Regression A Data Odyssey

6. Monte Carlo Simulation MIT OpenCourseWare

Friedman's H-statistic for Analysing Interactions | Maths and Intuition A Data Odyssey

Visual Calculations in Power BI - DAX Made Easy! [Full Course] Pragmatic Works

An introduction to LIME for local interpretations | Intuition and Algorithm | A Data Odyssey

Learning Dominant Dynamics for Continuum Robot Control (John Alora, PhD Defense) Stanford Autonomous Systems Laboratory

PDPs and ICE Plots | Python Code | scikit-learn Package A Data Odyssey

Accumulated Local Effect Plots (ALEs) | Explanation & Python Code A Data Odyssey

Visualising software architecture with the C4 model - Simon Brown, Agile on the Beach 2019 Agile on the Beach