Pushing the docs to dev/ for branch: main, commit 1d5e692ecac90058a427845f79b0744ac11e6dd5

Author: sklearn-ci

dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot Ridge coefficients as a function of the regularization\n\nShows the effect of collinearity in the coefficients of an estimator.\n\n.. currentmodule:: sklearn.linear_model\n\n:class:`Ridge` Regression is the estimator used in this example.\nEach color represents a different feature of the\ncoefficient vector, and this is displayed as a function of the\nregularization parameter.\n\nThis example also shows the usefulness of applying Ridge regression\nto highly ill-conditioned matrices. For such matrices, a slight\nchange in the target variable can cause huge variances in the\ncalculated weights. In such cases, it is useful to set a certain\nregularization (alpha) to reduce this variation (noise).\n\nWhen alpha is very large, the regularization effect dominates the\nsquared loss function and the coefficients tend to zero.\nAt the end of the path, as alpha tends toward zero\nand the solution tends towards the ordinary least squares, coefficients\nexhibit big oscillations. In practise it is necessary to tune alpha\nin such a way that a balance is maintained between both.\n"
+        "\n# Plot Ridge coefficients as a function of the regularization\n\nShows the effect of collinearity in the coefficients of an estimator.\n\n.. currentmodule:: sklearn.linear_model\n\n:class:`Ridge` Regression is the estimator used in this example.\nEach color represents a different feature of the\ncoefficient vector, and this is displayed as a function of the\nregularization parameter.\n\nThis example also shows the usefulness of applying Ridge regression\nto highly ill-conditioned matrices. For such matrices, a slight\nchange in the target variable can cause huge variances in the\ncalculated weights. In such cases, it is useful to set a certain\nregularization (alpha) to reduce this variation (noise).\n\nWhen alpha is very large, the regularization effect dominates the\nsquared loss function and the coefficients tend to zero.\nAt the end of the path, as alpha tends toward zero\nand the solution tends towards the ordinary least squares, coefficients\nexhibit big oscillations. In practice it is necessary to tune alpha\nin such a way that a balance is maintained between both.\n"
       ]
     },
     {
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "ax = plt.gca()\n\nax.plot(alphas, coefs)\nax.set_xscale(\"log\")\nax.set_xlim(ax.get_xlim()[::-1])  # reverse axis\nplt.xlabel(\"alpha\")\nplt.ylabel(\"weights\")\nplt.title(\"Ridge coefficients as a function of the regularization\")\nplt.axis(\"tight\")\nplt.show()"
+        "ax = plt.gca()\n\nax.plot(alphas, coefs)\nax.set_xscale(\"log\")\nax.set_xlim(ax.get_xlim()[::-1])  # reverse axis\nplt.xlabel(\"alpha\")\nplt.ylabel(\"weights\")\nplt.title(\"Ridge Coefficients vs Regularization Strength (alpha)\")\nplt.axis(\"tight\")\nplt.legend(\n    [f\"Feature {i + 1}\" for i in range(X.shape[1])], loc=\"best\", fontsize=\"small\"\n)\nplt.show()"
       ]
     }
   ],

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -22,7 +22,7 @@
 squared loss function and the coefficients tend to zero.
 At the end of the path, as alpha tends toward zero
 and the solution tends towards the ordinary least squares, coefficients
-exhibit big oscillations. In practise it is necessary to tune alpha
+exhibit big oscillations. In practice it is necessary to tune alpha
 in such a way that a balance is maintained between both.
 
 """
@@ -63,6 +63,9 @@
 ax.set_xlim(ax.get_xlim()[::-1])  # reverse axis
 plt.xlabel("alpha")
 plt.ylabel("weights")
-plt.title("Ridge coefficients as a function of the regularization")
+plt.title("Ridge Coefficients vs Regularization Strength (alpha)")
 plt.axis("tight")
+plt.legend(
+    [f"Feature {i + 1}" for i in range(X.shape[1])], loc="best", fontsize="small"
+)
 plt.show()