1.4 KiB
W2D05 Piscine AI - Data Science
Model selection methodology
If you finished yesterday's exercises you should be able to train several Machine Learning algorithms and to choose one returned by GridSearchCV. GridSearchCV returns the model that gives the best score on the test set. Yesterday, as I told you, I changed the cv parameter to compute the GridSearch with a train set and a test set.
It means that the selected model is based on one single measure. What if, by luck, we predict correctly on that section ? What if the best model is bad ? What if I could have selected a better model ?
We will answer these questions today ! The topics we will cover are the one of the most important in Machine Learning.
Exercises of the day
- Exercise 1 K-Fold
- Exercise 2 Cross validation (k-fold)
- Exercise 3 GridsearchCV
- Exercise 4 Validation curve and Learning curve
Virtual Environment
- Python 3.x
- NumPy
- Pandas
- Jupyter or JupyterLab
Version of Pandas I used to do the exercises: 1.0.1. I suggest to use the most recent one.
Resources
Must read before to start the exercises