diff --git a/book_equations.ipynb b/book_equations.ipynb index 5e75028..b85af87 100644 --- a/book_equations.ipynb +++ b/book_equations.ipynb @@ -531,43 +531,43 @@ }, "cell_type": "markdown", "source": [ - "# Chapter 6\n", - "**Equation 6-1: Gini impurity**\n", + "# 6장\n", + "**식 6-1: 지니 불순도**\n", "\n", "$\n", "G_i = 1 - \\sum\\limits_{k=1}^{n}{{p_{i,k}}^2}\n", "$\n", "\n", "\n", - "**Equation 6-2: CART cost function for classification**\n", + "**식 6-2: 분류에 대한 CART 비용 함수**\n", "\n", "$\n", "\\begin{split}\n", "&J(k, t_k) = \\dfrac{m_{\\text{left}}}{m}G_\\text{left} + \\dfrac{m_{\\text{right}}}{m}G_{\\text{right}}\\\\\n", - "&\\text{where }\\begin{cases}\n", - "G_\\text{left/right} \\text{ measures the impurity of the left/right subset,}\\\\\n", - "m_\\text{left/right} \\text{ is the number of instances in the left/right subset.}\n", + "&\\text{여기서 }\\begin{cases}\n", + "G_\\text{left/right} \\text{ 는 왼쪽/오른쪽 서브셋의 불순도}\\\\\n", + "m_\\text{left/right} \\text{ 는 왼쪽/오른쪽 서브셋의 불순도}\n", "\\end{cases}\n", "\\end{split}\n", "$\n", "\n", - "**Entropy computation example (page 173):**\n", + "**엔트로피 계산 예 (232 페이지):**\n", "\n", - "$ -\\frac{49}{54}\\log(\\frac{49}{54}) - \\frac{5}{54}\\log(\\frac{5}{54}) $\n", + "$ -\\frac{49}{54}\\log_2(\\frac{49}{54}) - \\frac{5}{54}\\log_2(\\frac{5}{54}) $\n", "\n", "\n", - "**Equation 6-3: Entropy**\n", + "**식 6-3: 엔트로피**\n", "\n", "$\n", - "H_i = -\\sum\\limits_{k=1 \\atop p_{i,k} \\ne 0}^{n}{{p_{i,k}}\\log(p_{i,k})}\n", + "H_i = -\\sum\\limits_{k=1 \\atop p_{i,k} \\ne 0}^{n}{{p_{i,k}}\\log_2(p_{i,k})}\n", "$\n", "\n", "\n", - "**Equation 6-4: CART cost function for regression**\n", + "**식 6-4: 회귀를 위한 CART 비용 함수**\n", "\n", "$\n", "J(k, t_k) = \\dfrac{m_{\\text{left}}}{m}\\text{MSE}_\\text{left} + \\dfrac{m_{\\text{right}}}{m}\\text{MSE}_{\\text{right}} \\quad\n", - "\\text{where }\n", + "\\text{여기서 }\n", "\\begin{cases}\n", "\\text{MSE}_{\\text{node}} = \\sum\\limits_{\\scriptstyle i \\in \\text{node}}(\\hat{y}_{\\text{node}} - y^{(i)})^2\\\\\n", "\\hat{y}_\\text{node} = \\dfrac{1}{m_{\\text{node}}}\\sum\\limits_{\\scriptstyle i \\in \\text{node}}y^{(i)}\n", @@ -582,16 +582,16 @@ }, "cell_type": "markdown", "source": [ - "# Chapter 7\n", + "# 7장\n", "\n", - "**Equation 7-1: Weighted error rate of the j^th^ predictor**\n", + "**식 7-1: j번째 예측기의 가중치가 적용된 에러율**\n", "\n", "$\n", "r_j = \\dfrac{\\displaystyle \\sum\\limits_{\\textstyle {i=1 \\atop \\hat{y}_j^{(i)} \\ne y^{(i)}}}^{m}{w^{(i)}}}{\\displaystyle \\sum\\limits_{i=1}^{m}{w^{(i)}}} \\quad\n", "\\text{where }\\hat{y}_j^{(i)}\\text{ is the }j^{\\text{th}}\\text{ predictor's prediction for the }i^{\\text{th}}\\text{ instance.}\n", "$\n", "\n", - "**Equation 7-2: Predictor weight**\n", + "**식 7-2: 예측기 가중치**\n", "\n", "$\n", "\\begin{split}\n", @@ -600,28 +600,28 @@ "$\n", "\n", "\n", - "**Equation 7-3: Weight update rule**\n", + "**식 7-3: 가중치 업데이트 규칙**\n", "\n", "$\n", "\\begin{split}\n", - "& \\text{ for } i = 1, 2, \\dots, m \\\\\n", "& w^{(i)} \\leftarrow\n", "\\begin{cases}\n", - "w^{(i)} & \\text{if }\\hat{y_j}^{(i)} = y^{(i)}\\\\\n", - "w^{(i)} \\exp(\\alpha_j) & \\text{if }\\hat{y_j}^{(i)} \\ne y^{(i)}\n", - "\\end{cases}\n", + "w^{(i)} & \\hat{y_j}^{(i)} = y^{(i)} \\text{ 일 때}\\\\\n", + "w^{(i)} \\exp(\\alpha_j) & \\hat{y_j}^{(i)} \\ne y^{(i)} \\text{ 일 때}\n", + "\\end{cases} \\\\\n", + "& \\text{여기서 } i = 1, 2, \\dots, m \\\\\n", "\\end{split}\n", "$\n", "\n", - "**In the text page 194:**\n", + "**256 페이지 본문 중에서:**\n", "\n", - "Then all the instance weights are normalized (i.e., divided by $ \\sum_{i=1}^{m}{w^{(i)}} $).\n", + "그런 다음 모든 샘플의 가중치를 정규화합니다(즉, $ \\sum_{i=1}^{m}{w^{(i)}} $으로 나눕니다).\n", "\n", "\n", - "**Equation 7-4: AdaBoost predictions**\n", + "**식 7-4: AdaBoost 예측**\n", "\n", "$\n", - "\\hat{y}(\\mathbf{x}) = \\underset{k}{\\operatorname{argmax}}{\\sum\\limits_{\\scriptstyle j=1 \\atop \\scriptstyle \\hat{y}_j(\\mathbf{x}) = k}^{N}{\\alpha_j}} \\quad \\text{where }N\\text{ is the number of predictors.}\n", + "\\hat{y}(\\mathbf{x}) = \\underset{k}{\\operatorname{argmax}}{\\sum\\limits_{\\scriptstyle j=1 \\atop \\scriptstyle \\hat{y}_j(\\mathbf{x}) = k}^{N}{\\alpha_j}} \\quad \\text{여기서 }N\\text{은 예측기 수}\n", "$\n", "\n", "\n"