---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
widget:
structuredData:
Hour:
- 0
- 1
- 2
Lag_1:
- 4.215
- 3.741
- 3.38
Lag_2:
- 3.939
- 4.215
- 3.741
Lag_3:
- 4.222
- 3.939
- 4.215
Lag_4:
- 4.568
- 4.222
- 3.939
Temperature:
- 20.45
- 19.5
- 18.75
Weekday:
- 4
- 4
- 4
Weekofyear:
- 1
- 1
- 1
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
Click to expand
| Hyperparameter | Value |
|--------------------------|---------------|
| bootstrap | True |
| ccp_alpha | 0.0 |
| criterion | squared_error |
| max_depth | 10 |
| max_features | 1.0 |
| max_leaf_nodes | |
| max_samples | |
| min_impurity_decrease | 0.0 |
| min_samples_leaf | 1 |
| min_samples_split | 2 |
| min_weight_fraction_leaf | 0.0 |
| n_estimators | 50 |
| n_jobs | |
| oob_score | False |
| random_state | 59 |
| verbose | 0 |
| warm_start | False |
RandomForestRegressor(max_depth=10, n_estimators=50, random_state=59)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestRegressor(max_depth=10, n_estimators=50, random_state=59)