Pushing the docs to _pst_preview/ for branch: new_web_theme, commit a9d8f103c7544ad52ece9057cc98b9208447ff8d

Author: sklearn-ci

_pst_preview/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 944134de34d7115406f3afe04b120a2c
+config: 1e442cc8803d396db4383208272980af
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_pst_preview/_changed.html

@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.18"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

_pst_preview/_downloads/006fc185672e58b056a5c134db26935c/plot_coin_segmentation.ipynb

@@ -14,6 +14,7 @@
 
 We also show the tree structure of a model built on all of the features.
 """
+
 # %%
 # First load the copy of the Iris dataset shipped with scikit-learn:
 from sklearn.datasets import load_iris

_pst_preview/_downloads/00ae629d652473137a3905a5e08ea815/plot_iris_dtc.py

@@ -81,7 +81,7 @@ def best_low_complexity(cv_results):
 pipe = Pipeline(
     [
         ("reduce_dim", PCA(random_state=42)),
-        ("classify", LinearSVC(random_state=42, C=0.01, dual="auto")),
+        ("classify", LinearSVC(random_state=42, C=0.01)),
     ]
 )
 

_pst_preview/_downloads/02d88d76c60b7397c8c6e221b31568dd/plot_grid_search_refit_callable.py

@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.18"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

_pst_preview/_downloads/02f111fb3dd79805b161e14c564184fc/plot_sgd_comparison.ipynb

@@ -21,7 +21,7 @@
 plt.figure(figsize=(10, 5))
 for i, C in enumerate([1, 100]):
     # "hinge" is the standard SVM loss
-    clf = LinearSVC(C=C, loss="hinge", random_state=42, dual="auto").fit(X, y)
+    clf = LinearSVC(C=C, loss="hinge", random_state=42).fit(X, y)
     # obtain the support vectors through the decision function
     decision_function = clf.decision_function(X)
     # we can also calculate the decision function manually

_pst_preview/_downloads/036b9372e2e7802453cbb994da7a6786/plot_linearsvc_support_vectors.py

@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.18"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

_pst_preview/_downloads/0486bf9e537e44cedd2a236d034bcd90/plot_pcr_vs_pls.ipynb

@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.18"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

_pst_preview/_downloads/055e8313e28f2f3b5fd508054dfe5fe0/plot_roc_crossval.ipynb

@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.18"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,