feat: random forest for s3e11 (#347)

rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/model_randomforest.py

@@ -0,0 +1,35 @@
+import pandas as pd
+from sklearn.ensemble import RandomForestRegressor
+
+
+def select(X: pd.DataFrame) -> pd.DataFrame:
+    # Ignore feature selection logic
+    return X
+
+
+def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_valid: pd.DataFrame):
+    """Define and train the Random Forest model. Merge feature_select"""
+    X_train = select(X_train)
+
+    rf_params = {
+        "n_estimators": 100,
+        "max_depth": 10,
+        "min_samples_split": 2,
+        "min_samples_leaf": 1,
+        "max_features": "sqrt",
+        "random_state": 2023,
+        "n_jobs": -1,
+        "verbose": 1,
+    }
+    model = RandomForestRegressor(**rf_params)
+    model.fit(X_train, y_train)
+    return model
+
+
+def predict(model, X_test):
+    """
+    Keep feature select's consistency.
+    """
+    X_test = select(X_test)
+    y_pred = model.predict(X_test)
+    return y_pred.reshape(-1, 1)