Gradient Boosting .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "29f60584-1357-45c7-9cbe-2ad759993932",
   "metadata": {},
   "source": [
    "# Breast cancer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7ba39982-4926-4eca-be7f-897c275e3196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successful!\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "print(\"Successful!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5b510302-42a5-4d13-b88f-da3a3cdd7fd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 30)\n"
     ]
    }
   ],
   "source": [
    "breast_cancer_data = load_breast_cancer()\n",
    "\n",
    "df = pd.DataFrame(data=breast_cancer_data.data,columns= breast_cancer_data.feature_names)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be620e8f-ec69-4fa2-beb1-0bcb27b400c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
      "0        17.99         10.38          122.80     1001.0          0.11840   \n",
      "1        20.57         17.77          132.90     1326.0          0.08474   \n",
      "2        19.69         21.25          130.00     1203.0          0.10960   \n",
      "3        11.42         20.38           77.58      386.1          0.14250   \n",
      "4        20.29         14.34          135.10     1297.0          0.10030   \n",
      "\n",
      "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
      "0           0.27760          0.3001              0.14710         0.2419   \n",
      "1           0.07864          0.0869              0.07017         0.1812   \n",
      "2           0.15990          0.1974              0.12790         0.2069   \n",
      "3           0.28390          0.2414              0.10520         0.2597   \n",
      "4           0.13280          0.1980              0.10430         0.1809   \n",
      "\n",
      "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
      "0                 0.07871  ...         25.38          17.33           184.60   \n",
      "1                 0.05667  ...         24.99          23.41           158.80   \n",
      "2                 0.05999  ...         23.57          25.53           152.50   \n",
      "3                 0.09744  ...         14.91          26.50            98.87   \n",
      "4                 0.05883  ...         22.54          16.67           152.20   \n",
      "\n",
      "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
      "0      2019.0            0.1622             0.6656           0.7119   \n",
      "1      1956.0            0.1238             0.1866           0.2416   \n",
      "2      1709.0            0.1444             0.4245           0.4504   \n",
      "3       567.7            0.2098             0.8663           0.6869   \n",
      "4      1575.0            0.1374             0.2050           0.4000   \n",
      "\n",
      "   worst concave points  worst symmetry  worst fractal dimension  \n",
      "0                0.2654          0.4601                  0.11890  \n",
      "1                0.1860          0.2750                  0.08902  \n",
      "2                0.2430          0.3613                  0.08758  \n",
      "3                0.2575          0.6638                  0.17300  \n",
      "4                0.1625          0.2364                  0.07678  \n",
      "\n",
      "[5 rows x 30 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9fba535c-5554-4cc2-908d-7f585872e78c",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "Inconsistent shape between the condition and the input (got (30, 1) and (30,))",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[42], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m null_checker \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39misnull()\u001b[38;5;241m.\u001b[39msum()\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m9\u001b[39m))\n\u001b[0;32m----> 4\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheatmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnull_checker\u001b[49m\u001b[43m,\u001b[49m\u001b[43mannot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mChecking for null values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[0;32m~/myenv/lib/python3.12/site-packages/seaborn/matrix.py:446\u001b[0m, in \u001b[0;36mheatmap\u001b[0;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[1;32m    365\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Plot rectangular data as a color-encoded matrix.\u001b[39;00m\n\u001b[1;32m    366\u001b[0m \n\u001b[1;32m    367\u001b[0m \u001b[38;5;124;03mThis is an Axes-level function and will draw the heatmap into the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    443\u001b[0m \n\u001b[1;32m    444\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    445\u001b[0m \u001b[38;5;66;03m# Initialize the plotter object\u001b[39;00m\n\u001b[0;32m--> 446\u001b[0m plotter \u001b[38;5;241m=\u001b[39m \u001b[43m_HeatMapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcenter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrobust\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mannot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    447\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mannot_kws\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcbar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcbar_kws\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxticklabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    448\u001b[0m \u001b[43m                      \u001b[49m\u001b[43myticklabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;66;03m# Add the pcolormesh kwargs here\u001b[39;00m\n\u001b[1;32m    451\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlinewidths\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m linewidths\n",
      "File \u001b[0;32m~/myenv/lib/python3.12/site-packages/seaborn/matrix.py:115\u001b[0m, in \u001b[0;36m_HeatMapper.__init__\u001b[0;34m(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;66;03m# Validate the mask and convert to DataFrame\u001b[39;00m\n\u001b[1;32m    113\u001b[0m mask \u001b[38;5;241m=\u001b[39m _matrix_mask(data, mask)\n\u001b[0;32m--> 115\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmasked_where\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplot_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;66;03m# Get good names for the rows and columns\u001b[39;00m\n\u001b[1;32m    118\u001b[0m xtickevery \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/myenv/lib/python3.12/site-packages/numpy/ma/core.py:1977\u001b[0m, in \u001b[0;36mmasked_where\u001b[0;34m(condition, a, copy)\u001b[0m\n\u001b[1;32m   1975\u001b[0m (cshape, ashape) \u001b[38;5;241m=\u001b[39m (cond\u001b[38;5;241m.\u001b[39mshape, a\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m   1976\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cshape \u001b[38;5;129;01mand\u001b[39;00m cshape \u001b[38;5;241m!=\u001b[39m ashape:\n\u001b[0;32m-> 1977\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInconsistent shape between the condition and the input\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1978\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (got \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (cshape, ashape))\n\u001b[1;32m   1979\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(a, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_mask\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m   1980\u001b[0m     cond \u001b[38;5;241m=\u001b[39m mask_or(cond, a\u001b[38;5;241m.\u001b[39m_mask)\n",
      "\u001b[0;31mIndexError\u001b[0m: Inconsistent shape between the condition and the input (got (30, 1) and (30,))"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x900 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "null_checker = df.isnull().sum()\n",
    "\n",
    "plt.figure(figsize=(10,9))\n",
    "sns.heatmap(null_checker,annot=True)\n",
    "plt.title(\"Checking for null values\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f6bb604c-62a3-4ba0-bd5c-d861081a7703",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target']= breast_cancer_data.target\n",
    "\n",
    "X = df[breast_cancer_data.feature_names]\n",
    "y = df['target']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fd5da5ac-5000-4b3a-a04b-1f5876433600",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.4,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dfc466f1-99fa-4de5-bd3f-d443c077f6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Gradient Boosting Classifier\n",
    "gbc = GradientBoostingClassifier(n_estimators= 100,learning_rate=0.2,random_state=42,max_depth=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e8e56f6b-71e0-4695-a838-6f335eb11842",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(learning_rate=0.2, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.2, random_state=42)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4f26cc2d-b854-455a-ac87-93b6bda42958",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = gbc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c7143a4a-39df-4deb-95c7-9b195afce4b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 95.18%\n"
     ]
    }
   ],
   "source": [
    "accuracy=accuracy_score(y_test,predictions)*100\n",
    "print(f\"Accuracy score: {accuracy:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aa7e05a6-af29-4ad0-b865-2f9daa8c225b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   malignant       0.95      0.91      0.93        80\n",
      "      benign       0.95      0.97      0.96       148\n",
      "\n",
      "    accuracy                           0.95       228\n",
      "   macro avg       0.95      0.94      0.95       228\n",
      "weighted avg       0.95      0.95      0.95       228\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report = classification_report(y_test,predictions,target_names=breast_cancer_data.target_names)\n",
    "print(\"Classification Report:\")\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "184196f4-9f80-4498-8d47-c551b8da05c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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