metadata
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: model.pkl
widget:
- structuredData:
blue_aggressiveness_score:
- 1.292
- 1
- 1.115
blue_avg_durability:
- 4.8
- 5.2
- 5.2
blue_avg_offense:
- 6.2
- 5.2
- 5.8
blue_early_mid_ratio:
- 1
- 0.667
- 1.5
blue_mid_late_ratio:
- 1.5
- 1.5
- 2
control_effects_diff:
- -22
- 0
- -2
difficulty_diff:
- 7
- 0
- 5
durability_diff:
- -8
- -11
- 3
offense_diff:
- 5
- 6
- 0
power_spike_diff:
- -1
- -1
- -3
red_aggressiveness_score:
- 0.812
- 0.541
- 1.261
red_avg_durability:
- 6.4
- 7.4
- 4.6
red_avg_offense:
- 5.2
- 4
- 5.8
red_early_mid_ratio:
- 1
- 1.333
- 0.5
red_mid_late_ratio:
- 4
- 3
- 1.333
Model description
This is a RandomForestClassifier model trained on mpl_id_s14 dataset.
Intended uses & limitations
[More Information Needed]
Training Procedure
[More Information Needed]
Hyperparameters
Click to expand
Hyperparameter | Value |
---|---|
bootstrap | True |
ccp_alpha | 0.0 |
class_weight | |
criterion | gini |
max_depth | 2 |
max_features | sqrt |
max_leaf_nodes | |
max_samples | |
min_impurity_decrease | 0.0 |
min_samples_leaf | 8 |
min_samples_split | 2 |
min_weight_fraction_leaf | 0.0 |
monotonic_cst | |
n_estimators | 50 |
n_jobs | |
oob_score | False |
random_state | 91 |
verbose | 0 |
warm_start | False |
Model Plot
RandomForestClassifier(max_depth=2, min_samples_leaf=8, n_estimators=50,random_state=91)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.
RandomForestClassifier(max_depth=2, min_samples_leaf=8, n_estimators=50,random_state=91)
Evaluation Results
[More Information Needed]
How to Get Started with the Model
[More Information Needed]
Model Card 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:
[More Information Needed]
Limitations
This model is only trained with MPL ID Season 14 data