Pushing the docs to dev/ for branch: main, commit 79afe9987cda921bb81c00381198217e9a32fac5

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -126,8 +126,8 @@ def plot_gpr_samples(gpr_model, n_samples, ax):
 )
 
 # %%
-# Rational Quadradtic kernel
-# ..........................
+# Rational Quadratic kernel
+# .........................
 from sklearn.gaussian_process.kernels import RationalQuadratic
 
 kernel = 1.0 * RationalQuadratic(length_scale=1.0, alpha=0.1, alpha_bounds=(1e-5, 1e15))
@@ -200,7 +200,7 @@ def plot_gpr_samples(gpr_model, n_samples, ax):
 kernel = ConstantKernel(0.1, (0.01, 10.0)) * (
     DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2
 )
-gpr = GaussianProcessRegressor(kernel=kernel, random_state=0)
+gpr = GaussianProcessRegressor(kernel=kernel, random_state=0, normalize_y=True)
 
 fig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8))
 

dev/_downloads/1bcb2039afa126da41f1cea42b4a5866/plot_gpr_prior_posterior.py

@@ -87,7 +87,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Rational Quadradtic kernel\n\n"
+        "### Rational Quadratic kernel\n\n"
       ]
     },
     {
@@ -156,7 +156,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.gaussian_process.kernels import ConstantKernel, DotProduct\n\nkernel = ConstantKernel(0.1, (0.01, 10.0)) * (\n    DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2\n)\ngpr = GaussianProcessRegressor(kernel=kernel, random_state=0)\n\nfig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8))\n\n# plot prior\nplot_gpr_samples(gpr, n_samples=n_samples, ax=axs[0])\naxs[0].set_title(\"Samples from prior distribution\")\n\n# plot posterior\ngpr.fit(X_train, y_train)\nplot_gpr_samples(gpr, n_samples=n_samples, ax=axs[1])\naxs[1].scatter(X_train[:, 0], y_train, color=\"red\", zorder=10, label=\"Observations\")\naxs[1].legend(bbox_to_anchor=(1.05, 1.5), loc=\"upper left\")\naxs[1].set_title(\"Samples from posterior distribution\")\n\nfig.suptitle(\"Dot-product kernel\", fontsize=18)\nplt.tight_layout()"
+        "from sklearn.gaussian_process.kernels import ConstantKernel, DotProduct\n\nkernel = ConstantKernel(0.1, (0.01, 10.0)) * (\n    DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2\n)\ngpr = GaussianProcessRegressor(kernel=kernel, random_state=0, normalize_y=True)\n\nfig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8))\n\n# plot prior\nplot_gpr_samples(gpr, n_samples=n_samples, ax=axs[0])\naxs[0].set_title(\"Samples from prior distribution\")\n\n# plot posterior\ngpr.fit(X_train, y_train)\nplot_gpr_samples(gpr, n_samples=n_samples, ax=axs[1])\naxs[1].scatter(X_train[:, 0], y_train, color=\"red\", zorder=10, label=\"Observations\")\naxs[1].legend(bbox_to_anchor=(1.05, 1.5), loc=\"upper left\")\naxs[1].set_title(\"Samples from posterior distribution\")\n\nfig.suptitle(\"Dot-product kernel\", fontsize=18)\nplt.tight_layout()"
       ]
     },
     {