metadata
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.
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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 |
Model Plot
The model plot is below.
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.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestRegressor(max_depth=10, n_estimators=50, random_state=59)
Evaluation Results
You can find the details about evaluation process and the evaluation results.
Metric | Value |
---|
How to Get Started with the Model
Use the code below to get started with the model.
Click to expand
[More Information Needed]
Model Card Authors
This model card is written by following authors:
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Model Card Contact
You can contact the model card authors through following channels: [More Information Needed]
Citation
Below you can find information related to citation.
BibTeX:
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