Machine_Learning2.ipynb

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    "# 나이브 베이즈 실습\n",
    "- 독립변수가 범주형 변수인 경우\n",
    "--> 다항분포 나이브 베이즈 ( Multinomail Naive Bayes )"
   ]
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   "source": [
    "## Multinomial Naive Bayes 라이브러리\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "## 난수 생성\n",
    "import numpy as np "
   ]
  },
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   "source": [
    "# 0부터 3까지 난수 생성, 변수 10개, 샘플 사이즈 4개\n",
    "mNB_train_X = np.random.randint(4,size=(4,10))\n",
    "mNB_train_y = np.array([1,2,3,4])\n"
   ]
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       "array([[0, 2, 2, 2, 1, 1, 2, 0, 0, 2],\n",
       "       [0, 3, 2, 0, 2, 2, 1, 2, 3, 2],\n",
       "       [1, 0, 0, 2, 3, 3, 2, 2, 1, 0],\n",
       "       [3, 1, 3, 3, 3, 1, 1, 0, 1, 2]])"
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    "mNB_train_X"
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       "array([1, 2, 3, 4])"
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    "mNB_train_y"
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     "text": [
      "[1]\n"
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      "text/plain": [
       "array([[0.91914482, 0.01554984, 0.00177523, 0.06353012]])"
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   "source": [
    "# 학습\n",
    "mNB = MultinomialNB()\n",
    "fitted_mNB = mNB.fit(mNB_train_X, mNB_train_y)\n",
    "# 첫 번째 값의 예측 결과(클래스) 확인\n",
    "print(fitted_mNB.predict(mNB_train_X[:1]))\n",
    "# 첫 번째 값의 각 클래스별 확률 확인\n",
    "fitted_mNB.predict_proba(mNB_train_X[:1])"
   ]
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   "execution_count": 7,
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       "MultinomialNB(class_prior=[0.1, 0.5, 0.1, 0.1])"
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    "clmNB_2 = MultinomialNB(class_prior = [0.1,0.5,0.1,0.1])\n",
    "clmNB_2.fit(mNB_train_X,mNB_train_y)"
   ]
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