Pushing the docs to dev/ for branch: main, commit a573692c6220f2f0d1cacdea51b3fbb4b891f9c9

Author: sklearn-ci

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -156,7 +156,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "First, we see that the model's predictions are more precise than the\nprevious model's: this new model is able to estimate the noise-free\nfunctional relationship.\n\nLooking at the kernel hyperparameters, we see that the best combination found\nhas a smaller noise level and shorter length scale than the first model.\n\nWe can inspect the Log-Marginal-Likelihood (LML) of\n:class:`~sklearn.gaussian_process.GaussianProcessRegressor`\nfor different hyperparameters to get a sense of the local minima.\n\n"
+        "First, we see that the model's predictions are more precise than the\nprevious model's: this new model is able to estimate the noise-free\nfunctional relationship.\n\nLooking at the kernel hyperparameters, we see that the best combination found\nhas a smaller noise level and shorter length scale than the first model.\n\nWe can inspect the negative Log-Marginal-Likelihood (LML) of\n:class:`~sklearn.gaussian_process.GaussianProcessRegressor`\nfor different hyperparameters to get a sense of the local minima.\n\n"
       ]
     },
     {
@@ -178,7 +178,7 @@
       },
       "outputs": [],
       "source": [
-        "vmin, vmax = (-log_marginal_likelihood).min(), 50\nlevel = np.around(np.logspace(np.log10(vmin), np.log10(vmax), num=20), decimals=1)\nplt.contour(\n    length_scale_grid,\n    noise_level_grid,\n    -log_marginal_likelihood,\n    levels=level,\n    norm=LogNorm(vmin=vmin, vmax=vmax),\n)\nplt.colorbar()\nplt.xscale(\"log\")\nplt.yscale(\"log\")\nplt.xlabel(\"Length-scale\")\nplt.ylabel(\"Noise-level\")\nplt.title(\"Log-marginal-likelihood\")\nplt.show()"
+        "vmin, vmax = (-log_marginal_likelihood).min(), 50\nlevel = np.around(np.logspace(np.log10(vmin), np.log10(vmax), num=20), decimals=1)\nplt.contour(\n    length_scale_grid,\n    noise_level_grid,\n    -log_marginal_likelihood,\n    levels=level,\n    norm=LogNorm(vmin=vmin, vmax=vmax),\n)\nplt.colorbar()\nplt.xscale(\"log\")\nplt.yscale(\"log\")\nplt.xlabel(\"Length-scale\")\nplt.ylabel(\"Noise-level\")\nplt.title(\"Negative log-marginal-likelihood\")\nplt.show()"
       ]
     },
     {

dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -151,7 +151,7 @@ def target_generator(X, add_noise=False):
 # Looking at the kernel hyperparameters, we see that the best combination found
 # has a smaller noise level and shorter length scale than the first model.
 #
-# We can inspect the Log-Marginal-Likelihood (LML) of
+# We can inspect the negative Log-Marginal-Likelihood (LML) of
 # :class:`~sklearn.gaussian_process.GaussianProcessRegressor`
 # for different hyperparameters to get a sense of the local minima.
 from matplotlib.colors import LogNorm
@@ -181,7 +181,7 @@ def target_generator(X, add_noise=False):
 plt.yscale("log")
 plt.xlabel("Length-scale")
 plt.ylabel("Noise-level")
-plt.title("Log-marginal-likelihood")
+plt.title("Negative log-marginal-likelihood")
 plt.show()
 
 # %%