Update README.md

README.md

@@ -233,6 +233,86 @@ The benchmark supports research into:
 
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+Quick Start
+# Quick Start
+
+This example demonstrates how to evaluate a simple model on one Clarus dataset.
+
+---
+
+## 1 Install dependencies
+
+Example environment:
+
+
+pip install pandas scikit-learn
+
+
+---
+
+## 2 Load the dataset
+
+
+train = data/train.csv
+test = data/test.csv
+
+
+---
+
+## 3 Train a simple baseline model
+
+Example using logistic regression:
+
+
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+
+train = pd.read_csv("data/train.csv")
+
+X = train.drop(columns=["scenario_id","label"])
+y = train["label"]
+
+model = LogisticRegression()
+model.fit(X, y)
+
+
+---
+
+## 4 Generate predictions
+
+
+test = pd.read_csv("data/test.csv")
+
+X_test = test.drop(columns=["scenario_id","label"])
+
+pred = model.predict(X_test)
+
+out = pd.DataFrame({
+"scenario_id": test["scenario_id"],
+"prediction": pred
+})
+
+out.to_csv("predictions.csv", index=False)
+
+
+---
+
+## 5 Evaluate predictions
+
+Run the official scorer:
+
+
+python scorer.py --predictions predictions.csv --truth data/test.csv
+
+
+The scorer returns:
+
+- accuracy
+- precision
+- recall
+- f1
+- confusion matrix
+
 # License
 
 MIT