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Update 2_6_Model_Tuning.ipynb

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Fixed typo.
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Carlos A. Iglesias 2022-02-28 12:51:18 +01:00 committed by GitHub
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ml1/2_6_Model_Tuning.ipynb
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@ -314,7 +314,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Changing manually the hyperparameters to find their optimal values is not practical. Instead, we can consider to find the optimal value of the parameters as an *optimization problem*. \n",
"Changing manually the hyperparameters to find their optimal values is not practical. Instead, we can consider to find the optimal value of the hyperparameters as an *optimization problem*. \n",
"\n",
"The sklearn comes with several optimization techniques for this purpose, such as **grid search** and **randomized search**. In this notebook we are going to introduce the former one."
]
@ -323,7 +323,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The sklearn provides an object that, given data, computes the score during the fit of an estimator on a parameter grid and chooses the parameters to maximize the cross-validation score. "
"The sklearn provides an object that, given data, computes the score during the fit of an estimator on a hyperparameter grid and chooses the hyperparameters to maximize the cross-validation score. "
]
},
{
@ -371,7 +371,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now evaluate the KFold with this optimized parameter as follows."
"We can now evaluate the KFold with this optimized hyperparameter as follows."
]
},
{
@ -431,7 +431,7 @@
"scores = ['precision', 'recall']\n",
"\n",
"for score in scores:\n",
" print(\"# Tuning hyper-hyperparameters for %s\" % score)\n",
" print(\"# Tuning hyperparameters for %s\" % score)\n",
" print()\n",
"\n",
" if score == 'precision':\n",