RandomForestRegressor

tools/sklearn/RandomForestRegressor.py

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+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import r2_score, mean_absolute_error
+import pandas as pd
+
+# Données d'exemple
+df = pd.DataFrame({
+    'sma5': [1, 2, 3, 4, 5],
+    'sma24': [2, 2, 2, 3, 4],
+    'close': [100, 102, 101, 105, 108]
+})
+df['future_gain'] = (df['close'].shift(-1) - df['close']) / df['close']
+
+X = df[['sma5', 'sma24']][:-1]
+y = df['future_gain'][:-1]
+
+model = RandomForestRegressor(n_estimators=200, random_state=42)
+model.fit(X, y)
+y_pred = model.predict(X)
+
+print("R²:", r2_score(y, y_pred))
+print("MAE:", mean_absolute_error(y, y_pred))
+print("Prédictions :", y_pred)