Upload folder using huggingface_hub

README.md

@@ -0,0 +1,63 @@
+
+---
+tags:
+- autotrain
+- tabular
+- classification
+- tabular-classification
+datasets:
+- dark-gbf-xgboost2/autotrain-data
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Tabular classification
+
+## Validation Metrics
+
+- logloss: 0.08323427141158712
+- accuracy: 0.98
+- mlogloss: 0.08323427141158712
+- f1_macro: 0.8266666666666665
+- f1_micro: 0.98
+- f1_weighted: 0.9793333333333333
+- precision_macro: 0.8666666666666666
+- precision_micro: 0.98
+- precision_weighted: 0.9833333333333333
+- recall_macro: 0.8333333333333333
+- recall_micro: 0.98
+- recall_weighted: 0.98
+- loss: 0.08323427141158712
+
+## Best Params
+
+- learning_rate: 0.16433034910560887
+- reg_lambda: 3.7914578973926436
+- reg_alpha: 2.806649620056883e-07
+- subsample: 0.7396301555452317
+- colsample_bytree: 0.9137471530067593
+- max_depth: 6
+- early_stopping_rounds: 383
+- n_estimators: 15000
+- eval_metric: mlogloss
+
+## Usage
+
+```python
+import json
+import joblib
+import pandas as pd
+
+model = joblib.load('model.joblib')
+config = json.load(open('config.json'))
+
+features = config['features']
+
+# data = pd.read_csv("data.csv")
+data = data[features]
+
+predictions = model.predict(data)  # or model.predict_proba(data)
+
+# predictions can be converted to original labels using label_encoders.pkl
+
+```

model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3aca3c209cf2095a513ce6d6dfd09f3ea02a2340fd80fecb20f16320e5f7c557
+size 2478558

target_encoders.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f9978cbbc1d743470eb07503a8f34d7f44732df9fed6c14972362621d83f584
+size 400

training_params.json

@@ -0,0 +1 @@
+{"data_path": "dark-gbf-xgboost2/autotrain-data", "model": "xgboost", "username": "Skreeauk", "seed": 742, "train_split": "train", "valid_split": "validation", "project_name": "dark-gbf-xgboost2", "push_to_hub": true, "id_column": "autotrain_id", "target_columns": ["autotrain_label"], "categorical_columns": null, "numerical_columns": null, "task": "classification", "num_trials": 100, "time_limit": 3600, "categorical_imputer": "most_frequent", "numerical_imputer": "median", "numeric_scaler": "robust"}