{
"cells": [
{
"cell_type": "markdown",
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"source": [
"Homepage: https://spkit.github.io\n",
"
Nikesh Bajaj : http://nikeshbajaj.in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decision Trees with shrinking capability from SpKit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**:In this notebook, we show the capability of decision tree from ***spkit*** to analysie the training and testing performace at each depth of a trained tree. After which, a trained tree can be shrink to any smaller depth, ***without retraining it***. So, by using Decision Tree from ***spkit***, you could choose a very high number for a **max_depth** (or just choose -1, for infinity) and analysis the parformance (accuracy, mse, loss) of training and testing (practically, a validation set) sets at each depth level. Once you decide which is the right depth, you could shrink your trained tree to that layer, without explicit training it again to with new depth parameter."
]
},
{
"cell_type": "markdown",
"metadata": {
"toc": true
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"source": [
"