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Update Arboles de Decision Mi Notebook.ipynb
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‎notebooks/Arboles de Decision Mi Notebook.ipynb‎

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"### es la importancia de los predictores "
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{
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"cell_type": "markdown",
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"id": "4ed6dcbe-9e95-4cc4-b8fe-13757512e41d",
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"metadata": {},
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"source": [
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"## Árboles de Regresión"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e85b066-8a10-496d-a150-781a8e5a14e9",
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"metadata": {},
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"source": [
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"chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www2.stat.duke.edu/~rcs46/lectures_2017/08-trees/08-tree-regression.pdf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"id": "afa2bc0f-ddde-4f9a-976c-47016f6060d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"id": "3dd68ead-37e9-402f-8451-1ee941e90771",
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"metadata": {},
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"outputs": [
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" <th></th>\n",
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" <th>crim</th>\n",
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" <th>lstat</th>\n",
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"text/plain": [
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" crim zn indus chas nox rm age dis rad tax ptratio \\\n",
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"0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 \n",
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"1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 \n",
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"2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 \n",
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"3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 \n",
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"4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 \n",
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"\n",
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" black lstat medv \n",
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"0 396.90 4.98 24.0 \n",
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"1 396.90 9.14 21.6 \n",
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"2 392.83 4.03 34.7 \n",
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"3 394.63 2.94 33.4 \n",
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"4 396.90 5.33 36.2 "
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data = pd.read_csv(\"../datasets/boston/Boston.csv\")\n",
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"data.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "88c7a8bf-0d46-43bc-9046-06fb28c784ff",
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"metadata": {},
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"source": [
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"https://www.kaggle.com/code/prasadperera/the-boston-housing-dataset"
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]
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},
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{
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"cell_type": "code",
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{
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"data": {
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"text/plain": [
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"(506, 14)"
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"output_type": "execute_result"
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}
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"source": [
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"data.shape"
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{
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"cell_type": "code",
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"execution_count": 71,
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"id": "3a861e3f-c183-4f01-aaf1-c388f1b531dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"colnames = data.columns.values.tolist()\n",
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"predictors = colnames[:13]\n",
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"target = colnames[13]\n",
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"X=data[predictors]\n",
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"Y = data[target]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "be2adede-53a0-4306-b4e2-00b1a6edb6d6",
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"metadata": {},
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"outputs": [],
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"source": []

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