ml4ec
1
Set up
1.1
Apps
1.2
Libraries
2
Introduction
2.1
Learning objectives
2.2
Some primers
2.3
Overfitting
2.4
Our modelling challenge
2.5
Data
2.5.1
Available variables
2.6
More primers
2.6.1
K-nearest neighbours
2.6.2
Random Forest
3
Data splitting
3.1
Reading and wrangling data
3.2
Splitting into testing and training sets
4
Pre-processing
4.1
Dealing with missingness and bad data
4.2
Standardization
4.3
More pre-processing
5
Model formulation
5.1
Formula notation
5.2
The generic
train()
5.3
Recipes
6
Model training
6.1
Hyperparameter tuning
6.2
Resampling
7
Exercises
7.1
Reading and cleaning
7.2
Data splitting
7.3
Linear model
7.3.1
Training
7.3.2
Prediction
7.4
KNN
7.4.1
Check data
7.4.2
Training
7.4.3
Prediction
7.4.4
Sample hyperparameters
7.5
Random forest
7.5.1
Training
7.5.2
Prediction
8
Solutions
8.1
Reading and cleaning
8.2
Data splitting
8.3
Linear model
8.3.1
Training
8.3.2
Prediction
8.4
KNN
8.4.1
Check data
8.4.2
Training
8.4.3
Prediction
8.4.4
Sample hyperparameters
8.5
Random forest
8.5.1
Training
8.5.2
Prediction
References
Published with bookdown
ml4ec - Machine Learning for Eddy Covariance data
References