From c9114cc79630187a674f049f09f9aa9221859dae Mon Sep 17 00:00:00 2001 From: cif2cif Date: Mon, 19 Apr 2021 17:47:22 +0200 Subject: [PATCH] Fixed broken link and bug of sklearn-deap with scikit 0.24 --- ml4/2_5_1_Exercise.ipynb | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/ml4/2_5_1_Exercise.ipynb b/ml4/2_5_1_Exercise.ipynb index 3b6cb59..9b02dc9 100644 --- a/ml4/2_5_1_Exercise.ipynb +++ b/ml4/2_5_1_Exercise.ipynb @@ -56,7 +56,7 @@ "metadata": {}, "source": [ "# Genetic Algorithms\n", - "In this section we are going to use the library DEAP [References](#References) for implementing a genetic algorithms.\n", + "In this section we are going to use the library DEAP [[References](#References)] for implementing a genetic algorithms.\n", "\n", "We are going to implement the OneMax problem as seen in class.\n", "\n", @@ -200,11 +200,13 @@ "source": [ "## 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], 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", "The same exercise (using the digits dataset) can be found in this [notebook](https://github.com/rsteca/sklearn-deap/blob/master/test.ipynb).\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", + "Note: There is a problem with the version 0.24 of scikit. Just comment the different approaches." ] }, { @@ -261,6 +263,15 @@ } ], "metadata": { + "datacleaner": { + "position": { + "top": "50px" + }, + "python": { + "varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])" + }, + "window_display": false + }, "kernelspec": { "display_name": "Python 3", "language": "python", @@ -276,7 +287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.9" }, "latex_envs": { "LaTeX_envs_menu_present": true,