From 265f47432e80dd0618eb298a95cb49148d92c421 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 29 Apr 2024 16:20:36 -0500
Subject: [PATCH] snapshot...
---
modules/machine_learning/classification.py | 5 +++--
1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/modules/machine_learning/classification.py b/modules/machine_learning/classification.py
index 87de1486..506db5e3 100644
--- a/modules/machine_learning/classification.py
+++ b/modules/machine_learning/classification.py
@@ -197,8 +197,8 @@ def k_nearest_neighbors_all(x, y, k_s=10):
plt.show()
-def decision_tree(x, y, criterion='entropy', max_depth=4):
- x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.2, random_state=4)
+def decision_tree(x_train, y_train, x_test, y_test, criterion='entropy', max_depth=4):
+ # x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.2, random_state=4)
print('Train set:', x_train.shape, y_train.shape)
print('Test set:', x_test.shape, y_test.shape)
@@ -211,6 +211,7 @@ def decision_tree(x, y, criterion='entropy', max_depth=4):
yhat = DT.predict(x_test)
yhat_prob = DT.predict_proba(x_test)
+ print(confusion_matrix(y_test, yhat, labels=[1, 0]))
print('Accuracy: ', "{:.4f}".format(accuracy_score(y_test, yhat)))
print('Jaccard Idx: ', "{:.4f}".format(jaccard_score(y_test, yhat)))
print('Precision: ', "{:.4f}".format(precision_score(y_test, yhat)))
--
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