From 2ade61411a014b3eed24bd2b382687d55233a9b5 Mon Sep 17 00:00:00 2001 From: leshe4ka46 Date: Sat, 15 Nov 2025 16:30:38 +0300 Subject: R(Cluster) --- .../3-04_logistic_regression.ipynb | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) (limited to 'Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb') diff --git a/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb index 3890ea7..4e4b2ab 100644 --- a/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb +++ b/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb @@ -205,7 +205,9 @@ "## Logistic Regression ##\n", "Logistic regression can be used to estimate the probability of an outcome as a function of some (assumed independent) inputs. In our case, we would like to estimate infection risk based on population members' age and sex.\n", "\n", - "Below we train a logistic regresion model. We first create a cuML logistic regression instance `logreg`. The `logreg.fit` method takes 2 arguments: the model's independent variables *X*, and the dependent variable *y*. Fit the `logreg` model using the `gdf` columns `age` and `sex` as *X* and the `infected` column as *y*." + "Below we train a logistic regresion model. We first create a cuML logistic regression instance `logreg`. The `logreg.fit` method takes 2 arguments: the model's independent variables *X*, and the dependent variable *y*. Fit the `logreg` model using the `gdf` columns `age` and `sex` as *X* and the `infected` column as *y*.\n", + "\n", + "1/(1+e^{-z}) sigmoid" ] }, { @@ -636,6 +638,13 @@ "logreg.fit(gdf[['age', 'sex']], gdf['infected'])" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, -- cgit v1.2.3