From 8f56d024f46b74303377ab44a8a66140488cd6af Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Thu, 21 Oct 2021 10:02:46 -0400 Subject: [PATCH] small edit for clarity --- 4-Classification/2-Classifiers-1/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md index 68877e6ac..e05a399f6 100644 --- a/4-Classification/2-Classifiers-1/README.md +++ b/4-Classification/2-Classifiers-1/README.md @@ -125,7 +125,7 @@ Let's see if we can reason our way through different approaches given the constr We will be using Scikit-learn to analyze our data. However, there are many ways to use logistic regression in Scikit-learn. Take a look at the [parameters to pass](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression). -Essentially there are two important parameters `multi_class` and `solver`, that we need to specify, when we ask Scikit-learn to perform a logistic regression. The `multi_class` value applies a certain behavior. The value of the solver is what algorithm to use. Not all solvers can be paired with all `multi_class` values. +Essentially there are two important parameters - `multi_class` and `solver` - that we need to specify, when we ask Scikit-learn to perform a logistic regression. The `multi_class` value applies a certain behavior. The value of the solver is what algorithm to use. Not all solvers can be paired with all `multi_class` values. According to the docs, in the multiclass case, the training algorithm: