---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: model.pkl
widget:
- structuredData:
blue_aggressiveness_score:
- 1.292
- 1.0
- 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
- 0.667
- 1.5
blue_mid_late_ratio:
- 1.5
- 1.5
- 2.0
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
- 4.0
- 5.8
red_early_mid_ratio:
- 1.0
- 1.333
- 0.5
red_mid_late_ratio:
- 4.0
- 3.0
- 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 |
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.
RandomForestClassifier(max_depth=2, min_samples_leaf=8, n_estimators=50,random_state=91)