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Update 2_5_1_Exercise.ipynb

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Added optional exercises.
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Carlos A. Iglesias 4 weeks ago committed by GitHub
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ml4/2_5_1_Exercise.ipynb
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@ -187,9 +187,9 @@
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Comparing\n", "## Comparing\n",
"Your task is modify the previous code to canonical GA configuration from Holland (look at the lesson's slides). In addition you should consult the [DEAP API](http://deap.readthedocs.io/en/master/api/tools.html#operators).\n", "Your task is to modify the previous code to canonical GA configuration from Holland (look at the lesson's slides). In addition you should consult the [DEAP API](http://deap.readthedocs.io/en/master/api/tools.html#operators).\n",
"\n", "\n",
"Submit your notebook and include a the modified code, and a comparison of the effects of these changes. \n", "Submit your notebook and include a modified code and a comparison of the effects of these changes. \n",
"\n", "\n",
"Discuss your findings." "Discuss your findings."
] ]
@ -198,7 +198,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Optimizing ML hyperparameters\n", "## Optional. Optimizing ML hyperparameters\n",
"\n", "\n",
"One of the applications of Genetic Algorithms is the optimization of ML hyperparameters. Previously we have used GridSearch from Scikit. Using (sklearn-deap)[[References](#References)], optimize the Titatic hyperparameters using both GridSearch and Genetic Algorithms. \n", "One of the applications of Genetic Algorithms is the optimization of ML hyperparameters. Previously we have used GridSearch from Scikit. Using (sklearn-deap)[[References](#References)], optimize the Titatic hyperparameters using both GridSearch and Genetic Algorithms. \n",
"\n", "\n",
@ -206,7 +206,7 @@
"\n", "\n",
"Submit a notebook where you include well-crafted conclusions about the exercises, discussing the pros and cons of using genetic algorithms for this purpose.\n", "Submit a notebook where you include well-crafted conclusions about the exercises, discussing the pros and cons of using genetic algorithms for this purpose.\n",
"\n", "\n",
"Note: There is a problem with the version 0.24 of scikit. Just comment the different approaches." "Note: There is a problem with Scikit version 0.24. Comment on the different approaches."
] ]
}, },
{ {
@ -222,7 +222,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Optimizing a ML pipeline with a genetic algorithm\n", "## Optional. Optimizing an ML pipeline with a genetic algorithm\n",
"\n", "\n",
"The library [TPOT](#References) optimizes ML pipelines and comes with a lot of (examples)[https://epistasislab.github.io/tpot/examples/] and even notebooks, for example for the [iris dataset](https://github.com/EpistasisLab/tpot/blob/master/tutorials/IRIS.ipynb).\n", "The library [TPOT](#References) optimizes ML pipelines and comes with a lot of (examples)[https://epistasislab.github.io/tpot/examples/] and even notebooks, for example for the [iris dataset](https://github.com/EpistasisLab/tpot/blob/master/tutorials/IRIS.ipynb).\n",
"\n", "\n",

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