From 736b2955dc9e9d5b6743a0c793fb679bb8f72da7 Mon Sep 17 00:00:00 2001
From: Uwe Schmitt <uwe.schmitt@id.ethz.ch>
Date: Thu, 4 Mar 2021 13:05:17 +0100
Subject: [PATCH] improvements
---
07_regression.ipynb | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/07_regression.ipynb b/07_regression.ipynb
index 89119f0..7c0455e 100644
--- a/07_regression.ipynb
+++ b/07_regression.ipynb
@@ -893,7 +893,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Some algorithms from scikit-learn\n",
+ "## Some algorithms from `scikit-learn`\n",
"\n",
"- `sklearn.linear_model.LinearRegression` is a linear regression method, which only works well for target values which can be described as a linear combination of feature values. This is also known as linear least squares method.\n",
"\n",
@@ -910,8 +910,8 @@
"\n",
"- `sklearn.tree.DecisionTreeRegressor` expands the concept of decision trees to regression [is documented here](https://scikit-learn.org/stable/modules/tree.html#regression).\n",
"\n",
- "- `sklearn.linear_model.TweedieRegressor`, `sklearn.linear_model.PoissonRegressor`, `sklearn.linear_model.GammaRegressor` offer so-called *Generalized Linear Models* (**GLM**) which might be of interest if your target values are bounded or discrete or if the error distribution is skewed. \n",
- " - This is usually the case when your target values are event-based discrete counts/frequencies, or continuous amounts, durations, costs/prices, or rates/probabilities. The [scikit-learn GLM tutorial](https://scikit-learn.org/stable/modules/linear_model.html#generalized-linear-regression) provides a formal insight as well as tips for choosing GLM with some use case examples. \n",
+ "- `sklearn.linear_model.TweedieRegressor`, `sklearn.linear_model.PoissonRegressor`, `sklearn.linear_model.GammaRegressor` offer so-called *Generalized Linear Models* (**GLM**) \n",
+ " - These models are usually of interest when your target values are event-based discrete counts/frequencies, or continuous amounts, durations, costs/prices, or rates/probabilities. The [scikit-learn GLM tutorial](https://scikit-learn.org/stable/modules/linear_model.html#generalized-linear-regression) provides a formal insight as well as tips for choosing GLM with some use case examples. \n",
" - Beyond that the [wikipedia article about generalized linear models](https://en.wikipedia.org/wiki/Generalized_linear_model#Intuition) gives a nice intuition and [this discussion](https://stats.stackexchange.com/questions/190763/how-to-decide-which-glm-family-to-use) provides a guide which GLM should be used when.\n",
" - For assessing and hyperparameter-optimization of such General Linear Models you should also use [suitable metrics from scikit-learn](https://scikit-learn.org/stable/modules/model_evaluation.html#mean-poisson-gamma-and-tweedie-deviances)."
]
--
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