Sky Objects Classifier - Random Forest-Part9.ipynb

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   "source": [
    "### Testeando y evaluando el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_labels= clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "\n",
    "accscore = accuracy_score(y_test, y_pred)\n",
    "f1score = f1_score(y_test, y_pred, average = None)\n"
   ]
  },
  {
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   "source": [
    "La precisión conjunta del test dataset se puede obtener a continuación:"
   ]
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   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
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     "text": [
      "Acc. Score:  93.03 %\n"
     ]
    }
   ],
   "source": [
    "print('Acc. Score: ', np.round(accscore*100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos visualizar la F1-Score para cada clase:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('STAR', 92.729439809296778)\n",
      "('GALAXY', 93.665768194070083)\n",
      "('QSO', 90.873786407766985)\n"
     ]
    }
   ],
   "source": [
    "for i in range(sorted_labels.__len__()):\n",
    "    print((inv_class_mapper[sorted_labels[i]], f1score[i]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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