From 1d9d018c95db05a62a2e1772503c30f6960b0cde Mon Sep 17 00:00:00 2001
From: Ramin RX7 <52785580+Ramin-RX7@users.noreply.github.com>
Date: Sat, 28 Jan 2023 21:44:14 +0330
Subject: [PATCH] updated lineplot() arguments

---
 2-Regression/4-Logistic/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index 1c39e9a6..a89ba6cd 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -276,8 +276,8 @@ from sklearn.metrics import roc_curve, roc_auc_score
 y_scores = model.predict_proba(X_test)
 # calculate ROC curve
 fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
-sns.lineplot([0, 1], [0, 1])
-sns.lineplot(fpr, tpr)
+sns.lineplot(x=[0, 1], y=[0, 1])
+sns.lineplot(x=fpr, y=tpr)
 ```
 Using Seaborn again, plot the model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. "ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis." Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly: