Forex Forecasting with Random Forests 3/N – Modeling and tuning

# # Create model with best params
seed = 17
model = RandomForestClassifier(**best_param, random_state=seed)
model.fit(X_train, Y_train)
pred = model.predict(X_test)

print('Accuracy: {}'.format(accuracy_score(Y_test, pred)))
a, b, c, d, e, f, g, h, i = confusion_matrix(Y_test, pred).ravel()
maguro = a+i
print('Number of maguro detected: {}'.format(maguro))
print('Big Win/Big Loss Ratio: {}'.format(win_loss(Y_test, pred)))

# # Create model with best params
seed = 17
model = RandomForestClassifier(**best_param, random_state=seed)
model.fit(X_train, Y_train)
pred = model.predict(X_test)

print('Accuracy: {}'.format(accuracy_score(Y_test, pred)))
a, b, c, d, e, f, g, h, i = confusion_matrix(Y_test, pred).ravel()
maguro = a+i
print('Number of maguro detected: {}'.format(maguro))
print('Big Win/Big Loss Ratio: {}'.format(win_loss(Y_test, pred)))

# # Create model with best params
seed = 17
model = RandomForestClassifier(**best_param, random_state=seed)
model.fit(X_train, Y_train)
pred = model.predict(X_test)

print('Accuracy: {}'.format(accuracy_score(Y_test, pred)))
a, b, c, d, e, f, g, h, i = confusion_matrix(Y_test, pred).ravel()
maguro = a+i
print('Number of maguro detected: {}'.format(maguro))
print('Big Win/Big Loss Ratio: {}'.format(win_loss(Y_test, pred)))

# # Create model with best params
seed = 17
model = RandomForestClassifier(**best_param, random_state=seed)
model.fit(X_train, Y_train)
pred = model.predict(X_test)

print('Accuracy: {}'.format(accuracy_score(Y_test, pred)))
a, b, c, d, e, f, g, h, i = confusion_matrix(Y_test, pred).ravel()
maguro = a+i
print('Number of maguro detected: {}'.format(maguro))
print('Big Win/Big Loss Ratio: {}'.format(win_loss(Y_test, pred)))

## Save model
pd.to_pickle(model, '')