advanced-algorithmic-trading

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)
rf = RandomForestRegressor(
n_estimators=step_factor*(i+1),
n_jobs=n_jobs,
random_state=random_state
)
rf.fit(X_train, y_train)
mse = mean_squared_error(y_test, rf.predict(X_test))
estimators[i] = step_factor*(i+1)
rf_mse[i] = mse
# Estimate the AdaBoost MSE over the full number
# of estimators, across a step size ("step_factor")
for i in range(0, axis_step):
print("Boosting Estimator: %d of %d..." % (
step_factor*(i+1), n_estimators)
)
boosting = AdaBoostRegressor(
DecisionTreeRegressor(),
n_estimators=step_factor*(i+1),
random_state=random_state,
learning_rate=0.01
)
boosting.fit(X_train, y_train)
mse = mean_squared_error(y_test, boosting.predict(X_test))
estimators[i] = step_factor*(i+1)
boosting_mse[i] = mse
# Plot the chart of MSE versus number of estimators
plt.figure(figsize=(8, 8))
plt.title(’Bagging, Random Forest and Boosting comparison’)
plt.plot(estimators, bagging_mse, ’b-’, color="black", label=’Bagging’)
plt.plot(estimators, rf_mse, ’b-’, color="blue", label=’Random Forest’)
plt.plot(estimators, boosting_mse, ’b-’, color="red", label=’AdaBoost’)
plt.legend(loc=’upper right’)
plt.xlabel(’Estimators’)
plt.ylabel(’Mean Squared Error’)
plt.show()