Testing Confusion Matrix for Tabular data in fast.ai

test_confusion_tabular.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from test_tabular_train import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[149   6]\n",
      " [ 31  14]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_confusion_tabular(learn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}