From 574cf5a7801a4f7a532256aaf778e147511a4efd Mon Sep 17 00:00:00 2001 From: Tomas Fabrizio Orsi Date: Thu, 11 May 2023 20:22:15 -0300 Subject: [PATCH] Commit final --- 7506R-1C2023-GRUPO09-CHP03.ipynb | 68 +++++++++++++++++++--- informe/7506R_TP1_GRUPO09_CHP3_REPORTE.tex | 2 +- 2 files changed, 62 insertions(+), 8 deletions(-) diff --git a/7506R-1C2023-GRUPO09-CHP03.ipynb b/7506R-1C2023-GRUPO09-CHP03.ipynb index f5b45ef..f40786e 100644 --- a/7506R-1C2023-GRUPO09-CHP03.ipynb +++ b/7506R-1C2023-GRUPO09-CHP03.ipynb @@ -6785,7 +6785,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 135, "id": "d6051594", "metadata": {}, "outputs": [], @@ -6817,7 +6817,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 136, "id": "3b7ffe74", "metadata": {}, "outputs": [], @@ -6840,7 +6840,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 137, "id": "132010e5", "metadata": {}, "outputs": [], @@ -6868,10 +6868,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 138, "id": "470ab235", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.1s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 20.9s finished\n", + "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 6.4s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 30.6s finished\n", + "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.2s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 5.8s finished\n" + ] + }, + { + "data": { + "text/plain": [ + "0.8035745678288896" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "stacking_model.fit(x_train,y_train)\n", "y_pred_st = stacking_model.predict(x_test)\n", @@ -6888,10 +6914,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 139, "id": "0fd71679", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "F1-Score: 0.8013276434329065\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(37.08333333333333, 0.5, 'Verdadero')" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "print('F1-Score: {}'.format(f1_score(y_test, y_pred_st, average='binary'))) \n", "confusion_voting = confusion_matrix(y_test, y_pred_st)\n", diff --git a/informe/7506R_TP1_GRUPO09_CHP3_REPORTE.tex b/informe/7506R_TP1_GRUPO09_CHP3_REPORTE.tex index ae0400a..5e9a663 100644 --- a/informe/7506R_TP1_GRUPO09_CHP3_REPORTE.tex +++ b/informe/7506R_TP1_GRUPO09_CHP3_REPORTE.tex @@ -92,7 +92,7 @@ \section*{Voting} \section*{Stacking} -El modelo stacking fue entrenado con los distintos modelos producidos a lo largo del análisis tuvo una precisión menor a lo esperado, los modelos bases usados en su construcción tienen una precisión superior a la predicción global hecha por el modelo, por lo cual, consideramos que tiene buenas métricas pero no representa una mejora considerable a los resultados anteriormente mencionados. +Para nuestra sorpresa, esto modelo tuvo una precisión menor a lo esperado. Los modelos bases usados en su construcción tienen una precisión superior a la predicción global hecha por el modelo, por lo cual, consideramos que tiene buenas métricas pero no representa una mejora considerable a los resultados anteriormente mencionados. \section*{General} En base a los resultados observados, se puede inferir que los modelos que emplean árboles de decisión presentan una adaptabilidad notable al generar predicciones sobre los datos. Sin embargo, es importante destacar que los demás modelos no muestran un rendimiento considerablemente inferior en comparación. Un problema que pudieron haber tenido nuestros modelos de ensamble híbrido, es que están usando modelos muy optimizados para sí mismos; esto puede haber introducido un sesgo alto.