Skip to main content

Ctrl+K

Introduction

Machine Learning Concepts

🎥 Introducing machine-learning concepts
✅ Quiz Intro.01

The predictive modeling pipeline

Module overview
Tabular data exploration
Fitting a scikit-learn model on numerical data

Categorical data

Handling categorical data

Decision tree models

Module overview
Intuitions on tree-based models
- 🎥 Intuitions on tree-based models
- ✅ Quiz M5.01

Combining categorical and numerical data

Combining categorical and numerical data

Selecting the best model

Module overview
🎥 Overfitting and Underfitting
✅ Quiz M2.01
🎥 Comparing train and test errors
📝 Exercise M2.01
📃 Solution for Exercise M2.01

Advanced topics

Module overview
🎥 Advanced topics
Resources for learning about advanced topics

Concluding remarks

🎥 Concluding remarks
Concluding remarks

Appendix

Glossary
Datasets description
Acknowledgement
Notebook timings
Table of contents

Repository
Suggest edit
Open issue

.md

Fitting a scikit-learn model on numerical data

Fitting a scikit-learn model on numerical data#

First model with scikit-learn
📝 Exercise M1.02
📃 Solution for Exercise M1.02
🎥 Intuitions on linear models
Working with numerical data
📝 Exercise M1.03
📃 Solution for Exercise M1.03
Preprocessing for numerical features
🎥 Validation of a model
Model evaluation using cross-validation
✅ Quiz M1.02

previous

📃 Solution for Exercise M1.01

next

First model with scikit-learn

By scikit-learn developers

© Copyright 2023.

Join the full MOOC for better learning!

Brought to you under a CC-BY License by Inria Learning Lab, scikit-learn @ La Fondation Inria, Inria Academy, with many thanks to the scikit-learn community as a whole!