Ch7_앙상블_학습과_랜덤_포레스트.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Ch7. 앙상블 학습과 랜덤 포레스트.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMPqzELlAYLSU9gM8+Kp9CD"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#7.1 투표 기반 분류기"
      ],
      "metadata": {
        "id": "eKj5wqGHaLHR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "더 좋은 분류기를 만드는 매우 간단한 방법은 각 분류기의 예측을 모아서 가장 많이 선택된 클래스를 예측하는 것이다.\n",
        "\n",
        "이렇게 다수결 투표로 정해지는 분류기를 hard voting 분류기라고 한다.\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "7zPueRSqaUzR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "각 분류기가 weak learner 일지라도 충분하게 많고 다양하다면 앙상블은 strong learner 가 될 수도 있다.\n",
        "\n",
        "- 모든 분류기가 완벽하게 독립적이고 오차에 상관관계가 없다면, 큰 수의 법칙이 적용된다.\n",
        "\n",
        "- 따라서 다양한 분류기를 각기 다른 알고리즘으로 학습시키면, 매우 다른 종류의 오차를 만들 가능성이 높기에 앙상블 모델의 정확도를 향상시킨다."
      ],
      "metadata": {
        "id": "d_hHUi27atmi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RwKXp6ZrlaXC"
      },
      "outputs": [],
      "source": [
        "# dataload\n",
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "X = iris['data'][:,(2,3)]   # 꽃잎 길이, 꽃잎 너비\n",
        "y = (iris['target'] == 2).astype(np.float64)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "log_clf = LogisticRegression()\n",
        "rnd_clf = RandomForestClassifier()\n",
        "svm_clf = SVC(probability=True)\n",
        "\n",
        "voting_clf = VotingClassifier(\n",
        "    estimators=[('lr', log_clf),('rf', rnd_clf),('svc', svm_clf)],\n",
        "    voting='soft')\n",
        "voting_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtD03bjgdjSD",
        "outputId": "0814eae9-ba1b-434a-8679-5199070285b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('lr', LogisticRegression()),\n",
              "                             ('rf', RandomForestClassifier()),\n",
              "                             ('svc', SVC(probability=True))],\n",
              "                 voting='soft')"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 각 분류기의 정확도를 확인해보자\n",
        "from sklearn.metrics import accuracy_score\n",
        "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
        "  clf.fit(X_train, y_train)\n",
        "  y_pred = clf.predict(X_test)\n",
        "  print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LECB6iglfznQ",
        "outputId": "c719410f-950e-45a5-ec19-9a7bf258c658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression 1.0\n",
            "RandomForestClassifier 1.0\n",
            "SVC 0.9666666666666667\n",
            "VotingClassifier 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "모든 분류기가 클래스의 확률을 예측할 수 있으면, 개별 분류기의 예측을 평균 내어 확률이 가장 높은 클래스를 예측할 수 있다. 이를 soft voting 이라고 한다. \n",
        "\n",
        "(SVC는 기본값에서는 클래스 확률을 제공하지 않으므로 probability 매개변수를 True로 지정.)"
      ],
      "metadata": {
        "id": "99ptYWkYgl6c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7.2 배깅과 페이스팅"
      ],
      "metadata": {
        "id": "yQ1X4q7OjdLF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "다양한 분류기를 만드는 또 다른 방법은 같은 알고리즘을 사용하고 훈련 세트의 서브셋을 무작위로 구성하여 분류기를 각기 다르게 학습시키는 것이다. 훈련 세트에서 중복을 허용하여 샘플링하는 방식을 bagging 이라 하며, 중복을 허용하지 않고 샘플링하는 방식을 pasting 이라고 한다."
      ],
      "metadata": {
        "id": "B3ai_VEojmO4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "앙상블의 모든 예측기의 예측을 모아서 새로운 샘플에 대한 예측을 만든다. 수집 함수는 전형적으로 분류일 때는 '통계적 최빈값' 이고 회귀에 대해서는 평균을 계산한다. "
      ],
      "metadata": {
        "id": "F9jUlpoTkA_I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7.2.1 사이킷런의 배깅과 페이스팅"
      ],
      "metadata": {
        "id": "_KgP8vpUkapv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 각 분류기는 훈련 세트에서 중복을 허용하여 무작위로 선택된 100개의 샘플로 훈련된다.\n",
        "\n",
        "- 페이스팅 : bootstrap = False 지정.\n",
        "\n",
        "- n_jobs : CPU 코어 수 지정"
      ],
      "metadata": {
        "id": "3Wwf4mQdkiSF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 결정 트리 분류기 500개의 앙상블을 훈련\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "bag_clf = BaggingClassifier(\n",
        "    DecisionTreeClassifier(), n_estimators=500,\n",
        "    max_samples=100, bootstrap=True, n_jobs=-1)\n",
        "bag_clf.fit(X_train, y_train)\n",
        "y_pred = bag_clf.predict(X_train)"
      ],
      "metadata": {
        "id": "U3p_Va7BgHNX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7.2.2 oob 평가"
      ],
      "metadata": {
        "id": "wLp-97nfl5en"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "BaggingClassifier 는 기본값으로 중복을 허용하여 훈련 세트의 크기만큼인 m개 샘플을 선택한다. 이는 평균적으로 각 예측기에 훈련 샘플의 63% 정도만 샘플링된다는 것을 의미한다.(1-exp^(-1)=63.212%)\n",
        "\n",
        "선택되지 않은 훈련 샘플을 oob(out-of-bag) 이라 하며 이 데이터를 검증 세트로 사용할 수 있다."
      ],
      "metadata": {
        "id": "9q_VQRaGmRGP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bag_clf_2 = BaggingClassifier(\n",
        "    DecisionTreeClassifier(), n_estimators=500,\n",
        "    max_samples=100, bootstrap=True, n_jobs=-1, oob_score=True)\n",
        "bag_clf_2.fit(X_train, y_train)\n",
        "print(bag_clf_2.oob_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B0DB1d_wltfW",
        "outputId": "2979c28a-3293-4d5b-8bf4-b28505fa851e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9583333333333334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 위의 모델의 테스트 데이터에 대한 예측 정확도 계산\n",
        "from sklearn.metrics import accuracy_score\n",
        "y_pred = bag_clf_2.predict(X_test)\n",
        "print(accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17TP46pZnB-H",
        "outputId": "8fb7fb90-9da5-4890-c3fb-27f55806388f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bag_clf_2.oob_decision_function_"
      ],
      "metadata": {
        "id": "k6dBOLEWncRj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96ac8693-9a04-433d-f02d-9dd213ba7791"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [1.        , 0.        ],\n",
              "       [0.8732746 , 0.1267254 ],\n",
              "       [0.80827408, 0.19172592],\n",
              "       [0.38864629, 0.61135371],\n",
              "       [1.        , 0.        ],\n",
              "       [1.        , 0.        ],\n",
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              "       [0.        , 1.        ],\n",
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              "       [1.        , 0.        ],\n",
              "       [0.        , 1.        ],\n",
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              "       [1.        , 0.        ],\n",
              "       [1.        , 0.        ],\n",
              "       [1.        , 0.        ],\n",
              "       [1.        , 0.        ],\n",
              "       [0.        , 1.        ],\n",
              "       [1.        , 0.        ],\n",
              "       [0.01421801, 0.98578199],\n",
              "       [1.        , 0.        ],\n",
              "       [1.        , 0.        ],\n",
              "       [1.        , 0.        ],\n",
              "       [1.        , 0.        ],\n",
              "       [1.        , 0.        ],\n",
              "       [1.        , 0.        ],\n",
              "       [1.        , 0.        ],\n",
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              "       [0.        , 1.        ],\n",
              "       [1.        , 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7.3 랜덤 패치와 랜덤 서브스페이스"
      ],
      "metadata": {
        "id": "OWx7-e6Snu33"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "BaggingClassifier 는 특성 샘플링도 지원한다. 샘플링은 max_features, bootstrap_features 매개변수로 조절된다.각 예측기는 무작위로 선택한 입력 특성의 일부분으로 훈련된다."
      ],
      "metadata": {
        "id": "5TMJuWcur5Fu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "훈련 특성과 샘플을 모두 샘플링하는 것을 '랜덤 패치 방식' 이라고 한다. \n",
        "\n",
        "훈련 샘플을 모두 사용하고 특성은 샘플링하는 것은 '랜덤 서브스페이스 방식' 이라고 한다.\n",
        "\n",
        "특성 샘플링은 더 다양한 예측기를 만들며 편향을 늘리는 대신 분산을 낮춘다."
      ],
      "metadata": {
        "id": "Qy5omWeGsUWE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7.4 랜덤 포레스트\n"
      ],
      "metadata": {
        "id": "_BDvYFfgsvL0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16,\n",
        "                                 n_jobs=-1)\n",
        "rnd_clf.fit(X_train, y_train)\n",
        "\n",
        "y_pred_rf = rnd_clf.predict(X_test)"
      ],
      "metadata": {
        "id": "9FzOWfspnsCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "랜덤 포레스트 알고리즘은 트리의 노드를 분할할 때 전체 특성 중에서 최선의 특성을 찾는 대신 무작위로 선택한 특성 후보 중에서 최적의 특성을 찾는 시긍로 무작위성을 더 주입한다. 이는 결국 편향을 손해보는 대신 분산을 낮춘다. "
      ],
      "metadata": {
        "id": "H21rSFSptXKS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##7.4.1 엑스트라 트리"
      ],
      "metadata": {
        "id": "zsC5VBMFtn7_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "모든 노드에서 특성마다 가장 최적의 임계값을 찾는 것이 트리 알고리즘에서 가장 시간이 많이 소용되는 작업 중 하나이므로 일반적인 랜덤 포레스트보다 엑스트라 트리가 훨씬 빠르다."
      ],
      "metadata": {
        "id": "chixR2TOty7V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7.4.2 특성 중요도"
      ],
      "metadata": {
        "id": "j8YXlPtft_4L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 랜덤 포레스트의 또 다른 장점은 특성의 상대적 중요도를 측정하기 쉽다는 것이다. \n",
        "\n",
        "- 사이킷런은 어떤 특성을 사용한 노드가 평균적으로 불순도를 얼마나 감소시키는지 확인하여 특성의 중요도를 측정한다. "
      ],
      "metadata": {
        "id": "w-elIJ1cuMTq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1)\n",
        "rnd_clf.fit(iris['data'], iris['target'])\n",
        "for name, score in zip(iris['feature_names'], rnd_clf.feature_importances_):\n",
        "  print(name, score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eOdXXwOytLNT",
        "outputId": "7c73126a-36ab-43ee-d462-21ef5b4ffff5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sepal length (cm) 0.0967889174251775\n",
            "sepal width (cm) 0.023772410188238197\n",
            "petal length (cm) 0.44788698251189324\n",
            "petal width (cm) 0.43155168987469117\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7.5 부스팅"
      ],
      "metadata": {
        "id": "mlM7yVd1u7ge"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "부스팅 방법의 아이디어는 앞의 모델을 보완해나가면서 일련의 예측기를 학습시키는 것이다. "
      ],
      "metadata": {
        "id": "MxZuOJtJvA1y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7.5.1 AdaBoost"
      ],
      "metadata": {
        "id": "3GRg_W0hvok0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "이전 예측기를 보완하는 새로운 예측기를 만드는 방법은 이전 모델이 과소적합했던 훈련 샘플의 가중치를 더 높이는 것이다. 이렇게 하면 새로운 예측기는 학습하기 어려운 샘플에 점점 더 맞춰지게 된다.\n",
        "\n",
        "이런 연속된 학습 기법은 경사 하강법과 비슷한 면이 있다. 경사하강법은 비용 함수를 최소화하기 위해 한 예측기의 모델 파라미터를 조정해가는 반면 에이다부스트는 점차 더 좋아지도록 앙상블에 예측기를 추가한다."
      ],
      "metadata": {
        "id": "LdMW0p9cvtY3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "모든 예측기가 훈련을 마치면 이 앙상블은 배깅이나 페이스팅과 비슷한 방식으로 예측을 만든다. 하지만 가중치가 적용된 훈련 세트의 전반적인 정확도에 따라 예측기마다 다른 가중치가 적용된다.\n",
        "\n",
        "예측을 할 때 에이다부스트는 단순히 모든 예측기의 예측을 계산하고 예측기 가중치 $ { α_j}$ 를 더해 예측 결과를 만든다. 가중치 합이 가장 큰 클래스가 예측 결과가 된다.\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "RgP4-hdE6csM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# AdaBoostClassifier 기본 추정기\n",
        "# from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "# ada_clf = AdaBoostClassifier(\n",
        "#     DecisionTreeClassifier(max_depth=1), n_estimators=200,\n",
        "#     algorithm='SAMME.R', learning_rate = 0.5)\n",
        "# ada_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "o7-_2Fovu1T8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7.5.2 그레이디언트 부스팅"
      ],
      "metadata": {
        "id": "LWpAQxmC9Tyl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "에이다부스트처럼 반복마다 샘프르이 가중치를 수정하는 대신 이전 예측기가 만든 residual error 에 새로운 예측기를 학습시킨다."
      ],
      "metadata": {
        "id": "RfoGgp5S91Rh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 간단한 2차식 형태의 데이터셋 생성\n",
        "import numpy as np\n",
        "np.random.seed(42)\n",
        "X = np.random.rand(100, 1) - 0.5\n",
        "y = 3*X[:, 0]**2 + 0.05*np.random.randn(100)"
      ],
      "metadata": {
        "id": "z1_1_kx7AVqo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 결정 트리를 기반 예측기로 사용하는 간단한 회귀 문제\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "tree_reg1 = DecisionTreeRegressor(max_depth=2)\n",
        "tree_reg1.fit(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TILunXp-Qvm",
        "outputId": "2575e13a-e28b-4ab9-b50d-a31ca3641ca8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=2)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 첫 번째 예측기에서 생긴 잔여 오차에 두 번째 DecisionTreeRegressor 를 훈련.\n",
        "y2 = y - tree_reg1.predict(X)\n",
        "tree_reg2 = DecisionTreeRegressor(max_depth=2)\n",
        "tree_reg2.fit(X, y2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BnXLqGu2-aU6",
        "outputId": "0d393f2d-8b32-4e7c-d2b1-7ec6b3d7ac3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=2)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 두 번째 예측기가 만든 잔여 오차에 세 번째 모델 훈련\n",
        "y3 = y2 - tree_reg2.predict(X)\n",
        "tree_reg3 = DecisionTreeRegressor(max_depth=2)\n",
        "tree_reg3.fit(X, y3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWQvYDgu-zU1",
        "outputId": "b786b0f7-11da-47dc-c0ae-03a5368b58b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=2)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = np.array([[0.8]])"
      ],
      "metadata": {
        "id": "Bglv4wr8AtEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 새로운 샘플을 만들려면 모든 트리의 예측을 더하면 된다.\n",
        "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
      ],
      "metadata": {
        "id": "ydn5p6eG_BBu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "각각의 예측기를 만들지 않고, 사이킷런의 GradientBoostingRegressor를 사용할 수 있다."
      ],
      "metadata": {
        "id": "dWomP9TC_ojr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3, learning_rate=1.0)\n",
        "gbrt.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQwx8UIL_eW1",
        "outputId": "435c362b-39b7-4a69-fe04-6d5ddf737838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ujwKbgSR_86q"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}