{ "cells": [ { "cell_type": "markdown", "metadata": { "school_cell_uuid": "2577e475d74c49d39d526854fc8e611e" }, "source": [ "## 5.4 분산 분석과 모형 성능" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "86b6d989ce9e4fccaa25a8ab5e68666e" }, "source": [ "### 분산 분석" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "school_cell_uuid": "5e86f952c67d4f3b8e04cb5eec45e282" }, "source": [ "선형회귀분석의 결과가 얼마나 좋은지는 단순히 잔차제곱합(RSS: Residula Sum of Square)으로 평가할 수 없다. 변수의 단위 즉, 스케일이 달라지면 회귀분석과 상관없이 잔차제곱합도 달라지기 때문이다.\n", "\n", "분산 분석(ANOVA: Analysis of Variance)은 종속변수의 분산과 독립변수의 분산간의 관계를 사용하여 선형회귀분석의 성능을 평가하고자 하는 방법이다. 분산 분석은 서로 다른 두 개의 선형회귀분석의 성능 비교에 응용할 수 있으며 독립변수가 카테고리 변수인 경우 각 카테고리 값에 따른 영향을 정량적으로 분석하는데도 사용된다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "9a024e84041e4d59bb8ff1215f21be1c" }, "source": [ "$\\bar{y}$를 종속 변수 $y$의 샘플 평균이라고 하자.\n", "\n", "$$\\bar{y}=\\frac{1}{N}\\sum_{i=1}^N y_i $$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "628d1c9b2ec24efdb1cc7e1ec24c01ac" }, "source": [ "종속 변수 $y$의 분산(샘플의 갯수로 나누지 않았으므로 정확하게는 분산이 아니지만 여기에서는 분산이라는 용어를 사용하자)을 나타내는 **TSS(total sum of square)**라는 값을 정의한다. **TSS는 종속변수값의 움직임의 범위**를 나타낸다.\n", "\n", "$$\\text{TSS} = \\sum_{i=1}^N (y_i-\\bar{y})^2 = (y - \\bar{y}1_N)^T(y - \\bar{y}1_N)$$\n", "\n", "위 식에서 $\\bar{y}1_N$는 $\\bar{y}$이라는 스칼라가 $N$번 반복된 브로드캐스팅 벡터다.\n", "\n", "\n", "마찬가지로 회귀 분석에 의해 예측한 값 $\\hat{y}$의 분산을 나타내는 **ESS(explained sum of squares)**,\n", "\n", "$$\\text{ESS}=\\sum_{i=1}^N (\\hat{y}_i -\\bar{\\hat{y}})^2 = (\\hat{y} - \\bar{\\hat{y}}1_N)^T(\\hat{y} - \\bar{\\hat{y}}1_N)$$\n", "\n", "잔차 $e$의 분산을 나타내는 **RSS(residual sum of squares)**도 정의할 수 있다.\n", "\n", "$$\\text{RSS}=\\sum_{i=1}^N (y_i - \\hat{y}_i)^2\\ = e^Te$$\n", "\n", "위 식에서 $\\bar{\\hat{y}}$는 모형 예측값 $\\hat{y}$의 평균이다.\n", "\n", "또한 **ESS는 모형에서 나온 예측값의 움직임의 범위**, **RSS는 잔차의 움직임의 범위, 즉 오차의 크기**를 뜻한다고 볼 수 있다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "0c0a3c34f958480583b9e2b5a0151708" }, "source": [ "만약 회귀모형이 상수항을 포함하여 올바르게 정의되었다면 잔차의 평균이 0이 된다. 즉, 종속변수의 평균과 모형 예측값의 평균이 같다.\n", "\n", "$$ \\bar{e} = \\bar{y} - \\bar{\\hat{y}} = 0$$\n", "\n", "$$ \\bar{y} = \\bar{\\hat{y}} $$\n", "\n", "그리고 이 분산값들 간에는 다음과 같은 관계가 성립한다. \n", "\n", "$$\\text{TSS} = \\text{ESS} + \\text{RSS}$$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "0882f84ed11f4393a7241255d0084ee4" }, "source": [ "이는 다음과 같이 증명할 수 있다.\n", "\n", "우선 회귀 분석으로 구한 가중치 벡터를 $\\hat{w}$, 독립 변수(설명 변수) $x$에 의한 종속 변수의 추정값을 $\\hat{y}$, 잔차를 $e$ 라고 하면 다음 식이 성립한다.\n", "\n", "$$ y = X\\hat{w} + e = \\hat{y} + e $$\n", "\n", "그리고 $X$의 평균 데이터 $\\bar{x}$\n", "\n", "$$ \n", "\\bar{x} = \\frac{1}{N}X^T1_N\n", "$$\n", "\n", "에 대한 예측값은 $y$의 평균데이터 $\\bar{y}$가 되므로 \n", "\n", "$$\n", "\\bar{x}^T\\hat{w} = \\bar{y}\n", "$$\n", "\n", "\n", "각 행의 값이 평균 데이터 $\\bar{x}$로 반복되는 행렬 $\\bar{X}$\n", "\n", "$$\n", "\\bar{X} = \\frac{1}{N}X^T1_N 1_N^T\n", "$$\n", "\n", "에 대한 예측값 벡터는 $\\bar{y}$값이 반복되는 벡터가 된다.\n", "\n", "$$\n", "\\bar{X}\\hat{w} = \\bar{y}1_N\n", "$$\n", "\n", "이를 위 식에 대입하면 \n", "\n", "$$\n", "\\hat{y} - \\bar{y}1_N = (X- \\bar{X})\\hat{w} \n", "$$\n", "\n", "가 된다.\n", "\n", "그런데 $\\bar{X}$와 잔차 $e$는 다음과 같은 직교 관계가 성립한다. \n", "\n", "$$ \\bar{X}^Te = \\frac{1}{N}X^T1_N 1_N^Te = \\frac{1}{N}X^T1_N 0 = 0 $$\n", "\n", "직교방정식\n", "\n", "$$\n", "X^Te = 0\n", "$$\n", "\n", "과 합치면 다음 식이 성립한다.\n", "\n", "$$ \\bar{X}^Te - X^Te = (\\bar{X} - X)^Te $$\n", "\n", "따라서\n", "\n", "$$\n", "\\begin{eqnarray}\n", "\\text{TSS} \n", "&=& (y - \\bar{y}1_N)^T(y - \\bar{y}1_N) \\\\\n", "&=& (\\hat{y} - \\bar{y}1_N + e)^T(\\hat{y} - \\bar{y}1_N + e) \\\\\n", "&=& (\\hat{y} - \\bar{y}1_N)^T(\\hat{y} - \\bar{y}1_N) + e^Te + 2(\\hat{y} - \\bar{y}1_N)^Te \\\\\n", "&=& (\\hat{y} - \\bar{y}1_N)^T(\\hat{y} - \\bar{y}1_N) + e^Te + 2\\hat{w}^T(X - \\bar{X})^Te \\\\\n", "&=& (\\hat{y} - \\bar{y}1_N)^T(\\hat{y} - \\bar{y}1_N) + e^Te \\\\\n", "&=& \\text{ESS} + \\text{RSS}\n", "\\end{eqnarray}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "69a3e069bd0d4dc5a3eaea862572787b" }, "source": [ "위 식이 말하는 바는 다음과 같다.\n", "\n", "> 모형 예측치의 움직임의 크기(분산)은 종속변수의 움직임의 크기(분산)보다 클 수 없다.\n", "\n", "> 모형의 성능이 좋을수록 모형 예측치의 움직임의 크기는 종속변수의 움직임의 크기와 비슷해진다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "c796514570984fcf8a8115d59940a2a6" }, "source": [ "간단한 1차원 데이터와 모형을 사용하여 이 식이 성립하는지 살펴보자." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "school_cell_uuid": "df98514025004af1bf2e4357b9167234" }, "outputs": [], "source": [ "from sklearn.datasets import make_regression\n", "\n", "X0, y, coef = make_regression(\n", " n_samples=100, n_features=1, noise=30, coef=True, random_state=0)\n", "dfX0 = pd.DataFrame(X0, columns=[\"X\"])\n", "dfX = sm.add_constant(dfX0)\n", "dfy = pd.DataFrame(y, columns=[\"Y\"])\n", "df = pd.concat([dfX, dfy], axis=1)\n", "\n", "model = sm.OLS.from_formula(\"Y ~ X\", data=df)\n", "result = model.fit()" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "c7cced34847641289de6b28adc88b02e" }, "source": [ "`RegressionResult` 타입 객체는 다음과 같이 분산분석과 관련된 속성값을 가진다." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "school_cell_uuid": "9d8f4a19e9a8432990e5c3bd819c85c5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TSS = 291345.7578983061\n", "ESS = 188589.61349210917\n", "RSS = 102754.33755137533\n", "ESS + RSS = 291343.9510434845\n", "R squared = 0.6473091780922586\n" ] } ], "source": [ "print(\"TSS = \", result.uncentered_tss)\n", "print(\"ESS = \", result.mse_model)\n", "print(\"RSS = \", result.ssr)\n", "print(\"ESS + RSS = \", result.mse_model + result.ssr)\n", "print(\"R squared = \", result.rsquared)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "school_cell_uuid": "1b549417d5de4d95a9adfd41769d72f9" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.distplot(y,\n", " kde=False, fit=sp.stats.norm, hist_kws={\"color\": \"r\", \"alpha\": 0.2}, fit_kws={\"color\": \"r\"},\n", " label=\"TSS\")\n", "sns.distplot(result.fittedvalues,\n", " kde=False, hist_kws={\"color\": \"g\", \"alpha\": 0.2}, fit=sp.stats.norm, fit_kws={\"color\": \"g\"},\n", " label=\"ESS\")\n", "sns.distplot(result.resid,\n", " kde=False, hist_kws={\"color\": \"b\", \"alpha\": 0.2}, fit=sp.stats.norm, fit_kws={\"color\": \"b\"},\n", " label=\"RSS\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "fd4d5737a0844dd2873fbf3c05717db7" }, "source": [ "### 결정계수(Coefficient of Determination)" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "83cea00a9df7491aab3a6303eeb3ae9a" }, "source": [ "위의 분산 관계식에서 모형의 성능을 나타내는 결정계수(Coefficient of Determination) $R^2$를 정의할 수 있다.\n", "\n", "$$R^2 \\equiv 1 - \\dfrac{\\text{RSS}}{\\text{TSS}}\\ = \\dfrac{\\text{ESS}}{\\text{TSS}}\\ $$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "f33a1c752ecd442f8b1e1e47978d2dc9" }, "source": [ "분산 관계식과 모든 분산값이 0보다 크다는 점을 이용하면 $R^2$의 값은 다음과 같은 조건을 만족함을 알 수 있다.\n", "\n", "$$0 \\leq R^2 \\leq 1$$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "5eb5e0c7bead471b8970e41aaf7c5bc5" }, "source": [ "여기에서 $R^2$가 0이라는 것은 오차의 분산 RSS가 최대이고 회귀분석 예측값의 분산 ESS가 0인 경우이므로 회귀분석 결과가 아무런 의미가 없다는 뜻이다.\n", "반대로 $R^2$가 1이라는 것은 오차의 분산 RSS가 0이고 회귀분석 예측의 분산 ESS가 TSS와 같은 경우이므로 회귀분석 결과가 완벽하다는 뜻이다.\n", "따라서 결정계수값은 회귀분석의 성능을 나타내는 수치라고 할 수 있다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "8743771052744a988f129b007031ddd1" }, "source": [ "### 분산 분석표" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "e22ce8312a3743f0a544575dc38b6d8c" }, "source": [ "분산 분석의 결과는 보통 다음과 같은 분산 분석표를 사용하여 표시한다. 아래의 표에서 $N$은 데이터의 갯수, $K$는 모수의 갯수를 뜻한다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "654460492333480c938200912a4e4896" }, "source": [ "\n", "| source | degree of freedom | sum of square | mean square | F test-statstics | p-value |\n", "|-|-|-|-|-|-|\n", "| Regression| $$K-1$$ | $$\\text{ESS}$$ | $$s_{\\hat{y}}^2 = \\dfrac{\\text{ESS}}{K-1}$$ | $$F=\\dfrac{s_{\\hat{y}}^2}{s_e^2} $$ | p-value |\n", "| Residual | $$N-K$$ | $$\\text{RSS}$$ | $$s_e^2= \\dfrac{\\text{RSS}}{N-K}$$ | |\n", "| Total | $$N-1$$ | $$\\text{TSS}$$ | $$s_y^2= \\dfrac{\\text{TSS}}{N-1}$$ | |\n", "| $R^2$ | | $$\\text{ESS} / \\text{TSS}$$ | | |\n" ] }, { "cell_type": "markdown", "metadata": { "sidetitle": true }, "source": [ "표 29.1 : 분산 분석표" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "76d670b1d1a0467d85018b7ed321d920" }, "source": [ "### 회귀 분석 F-검정과 분산 분석의 관계" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "f952b694aae044eeaf62952eaa6a9868" }, "source": [ "이러한 모양의 표를 사용하는 이유는 분산 분석의 결과를 이용하여 회귀 분석 F-검정에 필요한 검정통계량을 구할 수 있기 때문이다. \n", "\n", "회귀 분석 F-검정의 원래 귀무 가설은 모든 계수 $w_i$가 $0$ 이라는 것이지만 이 때는 모형이 아무런 의미가 없으므로 결정계수 값도 0이 된다 \n", "\n", "$$ H_0: R^2 = 0 $$\n", "\n", "이 때 $\\hat{w}$값은 기대값이 0인 정규 분포에서 나온 표본이므로 예측값 $\\hat{y} = \\hat{w}^T x$는 정규 분포의 선형 조합이라서 마찬가지로 정규 분포를 따른다. 그리고 잔차(residual)는 오차(disturbance)의 선형 변환으로 정규 분포를 따르므로 ESS와 RSS의 비율은 F 분포를 따른다.\n", "\n", "$$ \\dfrac{\\text{ESS}}{K-1} \\div \\dfrac{\\text{RSS}}{N-K} \\sim F(K-1, N-K) $$\n", "\n", "따라서 이 값을 회귀 분석 F-검정의 검정통계량으로 사용할 수 있다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "d58f96b3cf7e430e9a739f3a4e6ecc0c" }, "source": [ "statsmodels 에서는 다음과 같이 `anova_lm` 명령을 사용하여 분산 분석표를 출력할 수 있다. 다만 이 명령을 사용하기 위해서는 모형을 `from_formula` 메서드로 생성하여야 한다.\n", "\n", "`anova_lm` 명령으로 구한 F 검정통계량과 유의확률은 모형 `summary` 명령으로 구한 `F-statistic` 및 `Prob (F-statistic)`과 일치한다." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "school_cell_uuid": "5b73fce8a26742658e991fddcdce6943" }, "outputs": [ { "data": { "text/html": [ "
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dfsum_sqmean_sqFPR(>F)
X1.0188589.613492188589.613492179.8637666.601482e-24
Residual98.0102754.3375511048.513648NaNNaN
\n", "
" ], "text/plain": [ " df sum_sq mean_sq F PR(>F)\n", "X 1.0 188589.613492 188589.613492 179.863766 6.601482e-24\n", "Residual 98.0 102754.337551 1048.513648 NaN NaN" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.stats.anova_lm(result)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "school_cell_uuid": "57d165140a2e444494ce221000fee0d8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Y R-squared: 0.647\n", "Model: OLS Adj. R-squared: 0.644\n", "Method: Least Squares F-statistic: 179.9\n", "Date: Sat, 03 Nov 2018 Prob (F-statistic): 6.60e-24\n", "Time: 10:03:56 Log-Likelihood: -488.64\n", "No. Observations: 100 AIC: 981.3\n", "Df Residuals: 98 BIC: 986.5\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept -2.4425 3.244 -0.753 0.453 -8.880 3.995\n", "X 43.0873 3.213 13.411 0.000 36.712 49.463\n", "==============================================================================\n", "Omnibus: 3.523 Durbin-Watson: 1.984\n", "Prob(Omnibus): 0.172 Jarque-Bera (JB): 2.059\n", "Skew: -0.073 Prob(JB): 0.357\n", "Kurtosis: 2.312 Cond. No. 1.06\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "print(result.summary())" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "ade6b33ddfb846be8f1c5614d1fdbfd6" }, "source": [ "### 결정 계수와 상관 계수" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "096c2fa20c104e8fb540b0a23f797389" }, "source": [ "$y$와 $\\hat{y}$의 샘플 상관계수 $r$의 제곱은 결정 계수 $R^2$와 같다. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "school_cell_uuid": "726fbaaae2b84492bd696682d3d399f6" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.jointplot(result.fittedvalues, y)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "51172ce40b3d4d7f99c791d57b33d740" }, "source": [ "### 상수항이 없는 모형의 경우" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "7e595fd3890a48499ed8f673e2b10213" }, "source": [ "모형에서 상수항을 지정하지 않은 경우에는 결정계수의 정의에 사용되는 TSS의 정의가 다음과 같이 달라진다.\n", "\n", "$$\\text{TSS} = \\sum_i y_i^2 = y^Ty $$\n", "\n", "즉, 실제 샘플평균과 상관없이 $\\bar{y} = $이라는 가정하에 TSS를 계산한다. 이렇게 정의하지 않으면 TSS = RSS + ESS 관계식이 성립하지 않아서 결정계수의 값이 1보다 커지게 된다.\n", "\n", "따라서 모형의 결정계수를 비교할 때 **상수항이 없는 모형과 상수항이 있는 모형은 직접 비교하면 안된다.**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "school_cell_uuid": "d73b2084863143d99cd61a25abf7d6ec" }, "outputs": [], "source": [ "X0, y, coef = make_regression(\n", " n_samples=100, n_features=1, noise=30, bias=100, coef=True, random_state=0)\n", "dfX = pd.DataFrame(X0, columns=[\"X\"])\n", "dfy = pd.DataFrame(y, columns=[\"Y\"])\n", "df = pd.concat([dfX, dfy], axis=1)\n", "\n", "model2 = sm.OLS.from_formula(\"Y ~ X + 0\", data=df)\n", "result2 = model2.fit()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "school_cell_uuid": "4c7a4312f1f2409ca00e23ff319e2067" }, "outputs": [ { "data": { "text/plain": [ "0.18768724705943896" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result2.rsquared" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "f326cad592944c0baa3089b501594182" }, "source": [ "### F 검정을 이용한 모형 비교" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "938ee04cbe19439c817970e3fef05c19" }, "source": [ "F 검정을 이용하면 다음과 같이 포함관계(nesting)에 있는 두 모형의 성능을 비교할 수 있다. \n", "\n", "* 전체 모형(Full Model): $$ y = w_0 + w_1 x_1 + w_2 x_2 + w_3 x_3 $$\n", "* 축소 모형(Reduced Model): $$ y = w_0 + w_1 x_1 $$\n" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "c7c827a14783474a976309751d7e4f5f" }, "source": [ "다음과 같은 귀무 가설을 검정하는 것은 위의 두 모형이 실질적으로 같은 모형이라는 가설을 검장하는 것과 같다.\n", "\n", "$$ H_0: w_2 = w_3 = 0 $$\n", "\n", "이 검정도 F 검정을 사용하여 할 수 있다. StatsModels에서는 `anova_lm` 명령에 두 모형의 result 객체를 인수로 넣어주면 이러한 검정을 할 수 있다. 인수를 넣어줄 때는 축소 모형(reduced model), 전체 모형(full model)의 순서로 넣어준다." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "school_cell_uuid": "9258203f96ea4b9f8008d8f2904e4536" }, "outputs": [], "source": [ "from sklearn.datasets import load_boston\n", "\n", "boston = load_boston()\n", "dfX0_boston = pd.DataFrame(boston.data, columns=boston.feature_names)\n", "dfy_boston = pd.DataFrame(boston.target, columns=[\"MEDV\"])\n", "dfX_boston = sm.add_constant(dfX0_boston)\n", "df_boston = pd.concat([dfX_boston, dfy_boston], axis=1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "school_cell_uuid": "641024d48127480fb198fc591b957ed6" }, "outputs": [ { "data": { "text/html": [ "
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1492.011078.7845782.02.5793740.0572740.944342
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" ], "text/plain": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 494.0 11081.363952 0.0 NaN NaN NaN\n", "1 492.0 11078.784578 2.0 2.579374 0.057274 0.944342" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_full = sm.OLS.from_formula(\n", " \"MEDV ~ CRIM + ZN + INDUS + NOX + RM + AGE + DIS + RAD + TAX + PTRATIO + B + LSTAT + CHAS\", data=df_boston)\n", "model_reduced = sm.OLS.from_formula(\n", " \"MEDV ~ CRIM + ZN + NOX + RM + DIS + RAD + TAX + PTRATIO + B + LSTAT + CHAS\", data=df_boston)\n", "\n", "sm.stats.anova_lm(model_reduced.fit(), model_full.fit())" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "13b6c0d451f04f33826c399b3c21e9a2" }, "source": [ "### F 검정을 사용한 변수 중요도 비교" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "e998b873aa7e4bb3bf16a1fdc2a1dcd8" }, "source": [ "F 검정은 각 독립변수의 중요도를 비교하기 위해 사용할 수 있다. \n", "방법은 전체 모형과 각 변수 하나만을 뺀 모형들의 성능을 비교하는 것이다. 이는 간접적으로 각 독립 변수의 영향력을 측정하는 것과 같다. 예를 들어 보스턴 집값 데이터에서 CRIM이란 변수를 뺀 모델과 전체 모델의 비교하는 검정을 하면 이 검정 결과는 CRIM변수의 중요도를 나타낸다." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "school_cell_uuid": "5459c1b3e56345bfa911724a59a33a6b" }, "outputs": [ { "data": { "text/html": [ "
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df_residssrdf_diffss_diffFPr(>F)
0493.011322.0042770.0NaNNaNNaN
1492.011078.7845781.0243.21969910.8011930.001087
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" ], "text/plain": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 493.0 11322.004277 0.0 NaN NaN NaN\n", "1 492.0 11078.784578 1.0 243.219699 10.801193 0.001087" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_full = sm.OLS.from_formula(\n", " \"MEDV ~ CRIM + ZN + INDUS + NOX + RM + AGE + DIS + RAD + TAX + PTRATIO + B + LSTAT + CHAS\", data=df_boston)\n", "model_reduced = sm.OLS.from_formula(\n", " \"MEDV ~ ZN + INDUS + NOX + RM + AGE + DIS + RAD + TAX + PTRATIO + B + LSTAT + CHAS\", data=df_boston)\n", "\n", "sm.stats.anova_lm(model_reduced.fit(), model_full.fit())" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "aabf0a30d0bd4c8786c43a88b6404278" }, "source": [ "`anova_lm` 명령에서는 `typ` 인수를 `2`로 지정하면 하나 하나의 변수를 뺀 축소 모형에서의 F 검정값을 한꺼번에 계산할 수 있다." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "school_cell_uuid": "a46ba7c34ffd4cd790eaeed2033ac6eb" }, "outputs": [ { "data": { "text/html": [ "
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sum_sqdfFPR(>F)
CRIM243.2196991.010.8011931.086810e-03
ZN257.4929791.011.4350587.781097e-04
INDUS2.5166681.00.1117637.382881e-01
NOX487.1556741.021.6341964.245644e-06
RM1871.3240821.083.1040121.979441e-18
AGE0.0618341.00.0027469.582293e-01
DIS1232.4124931.054.7304576.013491e-13
RAD479.1539261.021.2788445.070529e-06
TAX242.2574401.010.7584601.111637e-03
PTRATIO1194.2335331.053.0349601.308835e-12
B270.6342301.012.0186515.728592e-04
LSTAT2410.8386891.0107.0634267.776912e-23
CHAS218.9703571.09.7242991.925030e-03
Residual11078.784578492.0NaNNaN
\n", "
" ], "text/plain": [ " sum_sq df F PR(>F)\n", "CRIM 243.219699 1.0 10.801193 1.086810e-03\n", "ZN 257.492979 1.0 11.435058 7.781097e-04\n", "INDUS 2.516668 1.0 0.111763 7.382881e-01\n", "NOX 487.155674 1.0 21.634196 4.245644e-06\n", "RM 1871.324082 1.0 83.104012 1.979441e-18\n", "AGE 0.061834 1.0 0.002746 9.582293e-01\n", "DIS 1232.412493 1.0 54.730457 6.013491e-13\n", "RAD 479.153926 1.0 21.278844 5.070529e-06\n", "TAX 242.257440 1.0 10.758460 1.111637e-03\n", "PTRATIO 1194.233533 1.0 53.034960 1.308835e-12\n", "B 270.634230 1.0 12.018651 5.728592e-04\n", "LSTAT 2410.838689 1.0 107.063426 7.776912e-23\n", "CHAS 218.970357 1.0 9.724299 1.925030e-03\n", "Residual 11078.784578 492.0 NaN NaN" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_boston = sm.OLS.from_formula(\n", " \"MEDV ~ CRIM + ZN + INDUS + NOX + RM + AGE + DIS + RAD + TAX + PTRATIO + B + LSTAT + CHAS\", data=df_boston)\n", "result_boston = model_boston.fit()\n", "sm.stats.anova_lm(result_boston, typ=2)" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "134479c7de0a49bb939495605dfbbc1e" }, "source": [ "이 값은 단일 계수 t 검정의 유의확률과 동일하다. 그 이유는 다음과 같은 t 분포와 F 분포의 동치 성질 때문이다.\n", "\n", "$$ t_n^2 = F_{(1, n)} $$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "809b1a9ac79a4bcda839f82fad339810" }, "source": [ "### 조정 결정 계수" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "bb2a470d0c9748db962a4294f2b6a8f3" }, "source": [ "선형 회귀 모형에서 독립 변수가 추가되면 결정 계수의 값은 항상 증가한다. 이는 다음과 같이 확인할 수 있다.\n", "\n", "종속 변수 $y$를 회귀 분석하기 위한 기존의 독립 변수가 $X$이고 여기에 추가적인 독립 변수 $z$가 더해졌을 때, 다음과 같은 관계가 성립한다.\n", "\n", "$$\n", "R^2_{Xz} = R^2_{X} + (1-R^2_{X})r^{\\ast 2}_{yz}\n", "$$\n", "\n", "여기에서 \n", "* $R^2_{X}$: 기존의 독립 변수 $X$를 사용한 경우의 결정 계수\n", "* $R^2_{Xz}$: 기존의 독립 변수 $X$와 추가적인 독립 변수 $z$를 모두 사용한 경우의 결정 계수\n", "* $r^{\\ast 2}_{yz}$: 추가적인 독립 변수 $z$와 종속 변수 $y$간의 상관 관계 계수\n", "\n", "\n", "이고 이 항목들은 모두 양수이므로 \n", "\n", "$$\n", "R^2_{Xz} \\geq R^2_{X}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "cc57abcae2cf4f189da669e0de3b77c9" }, "source": [ "이러한 독립 변수 추가 효과를 상쇄시키기 위한 다양한 기준들이 제시되었다. 그 중 하나가 다음과 같이 독립 변수의 갯수 $K$에 따라 결정 계수의 값을 조정하는 조정 결정 계수이다\n", "\n", "$$\n", "R_{adj}^2 = 1 - \\frac{n-1}{n-K}(1-R^2) = \\dfrac{(n-1)R^2 +1-K}{n-K}\n", "$$\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "1804a76e91bc471cbdaf4988ba437d50" }, "source": [ "### 정보량 규준" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "9294f4c662f64b5390a7b6abc51c552b" }, "source": [ "조정 결정 계수와 함께 많이 쓰이는 모형 비교 기준은 최대 우도에 독립 변수의 갯수에 대한 손실(penalty)분을 반영하는 방법이다. \n", "이를 정보량 규준(information criterion)이라고 하며 손실 가중치의 계산 법에 따라 AIC (Akaike Information Criterion)와 BIC (Bayesian Information Criterion) 두 가지를 사용한다.\n", "\n", "AIC는 모형과 데이터의 확률 분포 사이의 Kullback-Leibler 수준을 가장 크게하기 위한 시도에서 나왔다. BIC는 데이터가 exponential family라는 가정하에 주어진 데이터에서 모형의 likelihood를 측정하기 위한 값에서 유도되었다. 둘 다 값이 작을 수록 올바른 모형에 가깝다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "d160bcdcd9d344059129cab4340f47fc" }, "source": [ "$$\n", "\\text{AIC} = -2\\log L + 2K\n", "$$\n", "\n", "$$\n", "\\text{BIC} = -2\\log L + K\\log n\n", "$$" ] } ], "metadata": { "celltoolbar": "Edit 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": "3.7.0" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }