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| #### Gradient Boosting Regressor (0.1134)#### | |
| gbregressor = GradientBoostingRegressor(learning_rate=0.1, n_estimators=180) | |
| gbregressor.fit(X_train, np.log(y_train)) | |
| y_pred = np.exp(gbregressor.predict(X_test)) | |
| all_pred = np.concatenate((all_pred, 0.05*y_pred)) | |
| #### Lasso LarsIC (0.119)#### | |
| lassolars = LassoLarsIC(criterion='aic') | |
| lassolars.fit(X_train, np.log(y_train)) | |
| y_pred = np.exp(lassolars.predict(X_test)) | |
| all_pred = np.vstack((all_pred, 0.05*y_pred)) | |
| #### Random Forest (0.12401)#### | |
| rf_regressor = RandomForestRegressor(n_estimators = 325, random_state = 0) | |
| rf_regressor.fit(X_train, np.log(y_train)) | |
| y_pred = np.exp(rf_regressor.predict(X_test)) | |
| all_pred = np.vstack((all_pred, 0.05*y_pred)) | |
| #### Bayesian Ridge (0.1054)#### | |
| bayesianridge = BayesianRidge(alpha_1=130, alpha_2=0.0017, lambda_1=0.00001, lambda_2=0.000001) | |
| bayesianridge.fit(X_train, np.log(y_train)) | |
| y_pred = np.exp(bayesianridge.predict(X_test)) | |
| all_pred = np.vstack((all_pred, 0.12*y_pred)) | |
| #### ElasticNET (0.10032)#### | |
| elasticnet = ElasticNet(alpha=0.011, l1_ratio=0.5) | |
| elasticnet.fit(X_train, np.log(y_train)) | |
| y_pred = np.exp(elasticnet.predict(X_test)) | |
| all_pred = np.vstack((all_pred, 0.21*y_pred)) | |
| #### Lasso (0.10026)#### | |
| lasso = Lasso(alpha=0.005) | |
| lasso.fit(X_train, np.log(y_train)) | |
| y_pred = np.exp(lasso.predict(X_test)) | |
| all_pred = np.vstack((all_pred, 0.24*y_pred)) | |
| #### Ridge Regressor (0.1054)#### | |
| ridge = Ridge(alpha=75) | |
| ridge.fit(X_train, np.log(y_train)) | |
| y_pred = np.exp(ridge.predict(X_test)) | |
| all_pred = np.vstack((all_pred, 0.14*y_pred)) | |
| #### XGBoost Regressor (0.10624)##### | |
| xg_reg = xgb.XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.4, | |
| gamma=0, learning_rate=0.07, max_delta_step=0, max_depth=3, | |
| min_child_weight=1.5, missing=None, n_estimators=400, nthread=-1, | |
| objective='reg:linear', reg_alpha=0.75, reg_lambda=0.45, | |
| scale_pos_weight=1, seed=42, silent=True, subsample=0.6) | |
| xg_reg.fit(X_train,np.log(y_train)) | |
| y_pred = np.exp(xg_reg.predict(X_test)) | |
| all_pred = np.vstack((all_pred, 0.14*y_pred)) | |
| y_pred = np.sum(all_pred, axis=0) |
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