Model description
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Intended uses & limitations
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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 |
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How to Get Started with the Model
Use the code below to get started with the model.
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Model Card Authors
This model card is written by following authors:
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Model Card Contact
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Citation
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BibTeX:
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