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Commit From AutoTrain

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  1. .gitattributes +3 -0
  2. README.md +50 -0
  3. config.json +1 -0
  4. model.joblib +3 -0
.gitattributes CHANGED
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README.md ADDED
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+ ---
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+ tags:
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+ - autotrain
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+ - tabular
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+ - classification
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+ - tabular-classification
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+ datasets:
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+ - abhishek/autotrain-data-iris-train
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+ co2_eq_emissions: 0.0006300767567816624
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+ ---
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+
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+ # Model Trained Using AutoTrain
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+
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+ - Problem type: Multi-class Classification
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+ - Model ID: 9705273
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+ - CO2 Emissions (in grams): 0.0006300767567816624
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+
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+ ## Validation Metrics
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+
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+ - Loss: 0.15987505325856152
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+ - Accuracy: 0.9
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+ - Macro F1: 0.899749373433584
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+ - Micro F1: 0.9
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+ - Weighted F1: 0.8997493734335841
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+ - Macro Precision: 0.9023569023569024
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+ - Micro Precision: 0.9
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+ - Weighted Precision: 0.9023569023569025
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+ - Macro Recall: 0.9
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+ - Micro Recall: 0.9
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+ - Weighted Recall: 0.9
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+
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+ ## Usage
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+
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+ ```python
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+ import json
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+ import joblib
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+ import pandas as pd
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+
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+ model = joblib.load('model.joblib')
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+ config = json.load(open('config.json'))
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+
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+ features = config['features']
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+
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+ # data = pd.read_csv("data.csv")
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+ data = data[features]
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+ data.columns = ["feat_" + str(col) for col in data.columns]
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+
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+ predictions = model.predict(data) # or model.predict_proba(data)
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+
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+ ```
config.json ADDED
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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 ADDED
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+ oid sha256:c7a75c36f87985171a1e5acae52a7e09a68b6dfff260fb7ec568267b747ca19b
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+ size 3338