Commit From AutoTrain

.gitattributes

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README.md

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+---
+tags:
+- autotrain
+- tabular
+- classification
+- tabular-classification
+datasets:
+- abhishek/autotrain-data-iris-train
+co2_eq_emissions: 0.0006300767567816624
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 9705273
+- CO2 Emissions (in grams): 0.0006300767567816624
+
+## Validation Metrics
+
+- Loss: 0.15987505325856152
+- Accuracy: 0.9
+- Macro F1: 0.899749373433584
+- Micro F1: 0.9
+- Weighted F1: 0.8997493734335841
+- Macro Precision: 0.9023569023569024
+- Micro Precision: 0.9
+- Weighted Precision: 0.9023569023569025
+- Macro Recall: 0.9
+- Micro Recall: 0.9
+- Weighted Recall: 0.9
+
+## 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]
+data.columns = ["feat_" + str(col) for col in data.columns]
+
+predictions = model.predict(data)  # or model.predict_proba(data)
+
+```

config.json

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+{"features": ["SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm"], "targets": ["target"], "model_type": "logistic_regression", "target_mapping": {"Iris-setosa": 0, "Iris-versicolor": 1, "Iris-virginica": 2}}

model.joblib

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