Tabular Regression
Scikit-learn
English
robotics
random-forest
pid-control
line-follower
raspberry-pi
Instructions to use satwikshreshth1/pid-ml-follower-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use satwikshreshth1/pid-ml-follower-model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("satwikshreshth1/pid-ml-follower-model", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Add trained model
Browse files- model_meta.json +31 -0
- rf_model_tuned.pkl +3 -0
model_meta.json
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{
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"feature_cols": [
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"pid_error",
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"pid_error_prev",
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"pid_error_delta",
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"pid_derivative",
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"ir_centroid",
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"ir_spread",
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"ir1_inv",
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"ir2_inv",
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"ir3_inv",
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"ir4_inv",
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"ir5_inv",
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"left_speed",
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"right_speed",
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"speed_diff",
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"loop_dt"
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],
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"target_col": "next_error",
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"n_estimators": 328,
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"max_depth": 22,
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"min_samples_leaf": 9,
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"max_features": "sqrt",
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"train_runs": 80,
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"test_runs": 20,
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"original_rmse": 0.018756,
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"tuned_rmse": 0.018731,
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"tuned_mae": 0.008295,
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"best_optuna_trial": 22,
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"optuna_trials": 50
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}
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rf_model_tuned.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:92e811ed43def8e962d3bf2bb371eff3260865c22d5817786376b4fdb9df1217
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size 431729752
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