From 9f46c534f7e6b36f00a1e4ff85dd8e2ab83c3ca2 Mon Sep 17 00:00:00 2001 From: "Carlos A. Iglesias" Date: Thu, 18 Apr 2024 18:04:43 +0200 Subject: [PATCH] Update 2_5_1_Exercise.ipynb Added optional exercises. --- ml4/2_5_1_Exercise.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ml4/2_5_1_Exercise.ipynb b/ml4/2_5_1_Exercise.ipynb index 9b02dc9..70ed781 100644 --- a/ml4/2_5_1_Exercise.ipynb +++ b/ml4/2_5_1_Exercise.ipynb @@ -187,9 +187,9 @@ "metadata": {}, "source": [ "## 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", - "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", "Discuss your findings." ] @@ -198,7 +198,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Optimizing ML hyperparameters\n", + "## Optional. Optimizing ML hyperparameters\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", "\n", @@ -206,7 +206,7 @@ "\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", - "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", "metadata": {}, "source": [ - "## Optimizing a ML pipeline with a genetic algorithm\n", + "## Optional. Optimizing an ML pipeline with a genetic algorithm\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", "\n",