Can someone help me with that?\nAs script bellow, my prediction test is returning with same target for all card_ids!!!! I made a test with train prediction and it is ok! Any help I will really apreciate:
\n\n1) Script:\nSel_Model = GBR\nNameM = 'GBR'
\n\nprint ('# # # # Prediction for Model: ', NameM, '# # # #')\nprint ('Shape X_train: ', X_train.shape)\nprint ('Shape X_test: ', X_test.shape)\nprint ('Shape y: ', y.shape)\nprint ('Model: ', Sel_Model)\nSel_Model.fit(X_train, y)\nyhat_train = Sel_Model.predict(X_train)\nyhat_test = Sel_Model.predict(X_test)\nprint ('# # # # Prediction train:', yhat_train.shape, yhat_train)\nprint ('# # # # Prediction test:', yhat_test.shape, yhat_test)\nFilename = 'Output_' + NameM + '.csv'\ndf_Output= pd.DataFrame()\ndf_Output['card_id'] = df_test_original['card_id']\ndf_Output['target'] = yhat_test\ndf_Output.to_csv(Filename, index = False)
\n\n2) Logs:
\n\nShape X_train: (201917, 9)\nShape X_test: (123623, 9)\nShape y: (201917,)\nModel: GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n max_leaf_nodes=None, min_impurity_decrease=0.0,\n min_impurity_split=None, min_samples_leaf=1,\n min_samples_split=2, min_weight_fraction_leaf=0.0,\n n_estimators=100, n_iter_no_change=None, presort='auto',\n random_state=None, subsample=1.0, tol=0.0001,\n validation_fraction=0.1, verbose=0, warm_start=False)
\n\n-1.81737442]
\n\nCan someone help me with that?\nAs script bellow, my prediction test is returning with same target for all card_ids!!!! I made a test with train prediction and it is ok! Any help I will really apreciate:
\n\n1) Script:\nSel_Model = GBR\nNameM = 'GBR'
\n\nprint ('# # # # Prediction for Model: ', NameM, '# # # #')\nprint ('Shape X_train: ', X_train.shape)\nprint ('Shape X_test: ', X_test.shape)\nprint ('Shape y: ', y.shape)\nprint ('Model: ', Sel_Model)\nSel_Model.fit(X_train, y)\nyhat_train = Sel_Model.predict(X_train)\nyhat_test = Sel_Model.predict(X_test)\nprint ('# # # # Prediction train:', yhat_train.shape, yhat_train)\nprint ('# # # # Prediction test:', yhat_test.shape, yhat_test)\nFilename = 'Output_' + NameM + '.csv'\ndf_Output= pd.DataFrame()\ndf_Output['card_id'] = df_test_original['card_id']\ndf_Output['target'] = yhat_test\ndf_Output.to_csv(Filename, index = False)
\n\n2) Logs:
\n\nShape X_train: (201917, 9)\nShape X_test: (123623, 9)\nShape y: (201917,)\nModel: GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n max_leaf_nodes=None, min_impurity_decrease=0.0,\n min_impurity_split=None, min_samples_leaf=1,\n min_samples_split=2, min_weight_fraction_leaf=0.0,\n n_estimators=100, n_iter_no_change=None, presort='auto',\n random_state=None, subsample=1.0, tol=0.0001,\n validation_fraction=0.1, verbose=0, warm_start=False)
\n\n-1.81737442]
\n\nI think I found what was wrong. I forgot the prediction field (target) in the Test Data Frame. I deleted it and now it worked. I believe Python should present some other error but ok. It is here for who got the same error! regards!!!
", "rawMarkdown": "I think I found what was wrong. I forgot the prediction field (target) in the Test Data Frame. I deleted it and now it worked. I believe Python should present some other error but ok. It is here for who got the same error! regards!!!", "votes": null } ], "comments": [ { "id": 456809, "author_name": "vicentegsantos", "author_url": "", "post_date": "01/16/2019 15:20:38", "content": "I think I found what was wrong. I forgot the prediction field (target) in the Test Data Frame. I deleted it and now it worked. I believe Python should present some other error but ok. It is here for who got the same error! regards!!!
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "456060": "Can someone help me with that?\nAs script bellow, my prediction test is returning with same target for all card_ids!!!! I made a test with train prediction and it is ok! Any help I will really apreciate:\n\n1) Script:\nSel_Model = GBR\nNameM = 'GBR'\n#RunModel(NameM, Sel_Model, df_test_original, X_train, X_test, df_train_original['target'])\n#def RunModel (ModelName, Model, Df_Test_Original, X_train, X_test, y):\n\nprint ('# # # # Prediction for Model: ', NameM, '# # # #')\nprint ('Shape X_train: ', X_train.shape)\nprint ('Shape X_test: ', X_test.shape)\nprint ('Shape y: ', y.shape)\nprint ('Model: ', Sel_Model)\nSel_Model.fit(X_train, y)\nyhat_train = Sel_Model.predict(X_train)\nyhat_test = Sel_Model.predict(X_test)\nprint ('# # # # Prediction train:', yhat_train.shape, yhat_train)\nprint ('# # # # Prediction test:', yhat_test.shape, yhat_test)\nFilename = 'Output_' + NameM + '.csv'\ndf_Output= pd.DataFrame()\ndf_Output['card_id'] = df_test_original['card_id']\ndf_Output['target'] = yhat_test\ndf_Output.to_csv(Filename, index = False)\n\n2) Logs:\n# # # # Prediction for Model: GBR # # # #\nShape X_train: (201917, 9)\nShape X_test: (123623, 9)\nShape y: (201917,)\nModel: GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n max_leaf_nodes=None, min_impurity_decrease=0.0,\n min_impurity_split=None, min_samples_leaf=1,\n min_samples_split=2, min_weight_fraction_leaf=0.0,\n n_estimators=100, n_iter_no_change=None, presort='auto',\n random_state=None, subsample=1.0, tol=0.0001,\n validation_fraction=0.1, verbose=0, warm_start=False)\n# # # # Prediction train: (201917,) [-0.8204383 0.38480537 0.71745215 ... 0.11407518 -4.65064395\n -1.81737442]\n# # # # Prediction test: (123623,) [0.00194469 0.00194469 0.00194469 ... 0.00194469 0.00194469 0.00194469]", "456809": "I think I found what was wrong. I forgot the prediction field (target) in the Test Data Frame. I deleted it and now it worked. I believe Python should present some other error but ok. It is here for who got the same error! regards!!!" }, "source": "meta" }