Pushing the docs to dev/ for branch: main, commit ed82f78769fb29b5bfa79dec2ddb9d0e7babd0d7

Author: sklearn-ci

dev/.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: 8a2948de0fee28e95ddaa7ff03489af3
+config: 86c83344095ab669d629406e95411e2c
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Poisson regression and non-normal loss\n\nThis example illustrates the use of log-linear Poisson regression on the\n[French Motor Third-Party Liability Claims dataset](https://www.openml.org/d/41214) from [1]_ and compares it with a linear\nmodel fitted with the usual least squared error and a non-linear GBRT model\nfitted with the Poisson loss (and a log-link).\n\nA few definitions:\n\n- A **policy** is a contract between an insurance company and an individual:\n  the **policyholder**, that is, the vehicle driver in this case.\n\n- A **claim** is the request made by a policyholder to the insurer to\n  compensate for a loss covered by the insurance.\n\n- The **exposure** is the duration of the insurance coverage of a given policy,\n  in years.\n\n- The claim **frequency** is the number of claims divided by the exposure,\n  typically measured in number of claims per year.\n\nIn this dataset, each sample corresponds to an insurance policy. Available\nfeatures include driver age, vehicle age, vehicle power, etc.\n\nOur goal is to predict the expected frequency of claims following car accidents\nfor a new policyholder given the historical data over a population of\npolicyholders.\n\n.. [1]  A. Noll, R. Salzmann and M.V. Wuthrich, Case Study: French Motor\n    Third-Party Liability Claims (November 8, 2018). [doi:10.2139/ssrn.3164764](http://dx.doi.org/10.2139/ssrn.3164764)\n"
+        "\n# Poisson regression and non-normal loss\n\nThis example illustrates the use of log-linear Poisson regression on the\n[French Motor Third-Party Liability Claims dataset](https://www.openml.org/d/41214) from [1]_ and compares it with a linear\nmodel fitted with the usual least squared error and a non-linear GBRT model\nfitted with the Poisson loss (and a log-link).\n\nA few definitions:\n\n- A **policy** is a contract between an insurance company and an individual:\n  the **policyholder**, that is, the vehicle driver in this case.\n\n- A **claim** is the request made by a policyholder to the insurer to\n  compensate for a loss covered by the insurance.\n\n- The **exposure** is the duration of the insurance coverage of a given policy,\n  in years.\n\n- The claim **frequency** is the number of claims divided by the exposure,\n  typically measured in number of claims per year.\n\nIn this dataset, each sample corresponds to an insurance policy. Available\nfeatures include driver age, vehicle age, vehicle power, etc.\n\nOur goal is to predict the expected frequency of claims following car accidents\nfor a new policyholder given the historical data over a population of\npolicyholders.\n\n.. [1]  A. Noll, R. Salzmann and M.V. Wuthrich, Case Study: French Motor\n    Third-Party Liability Claims (November 8, 2018). [doi:10.2139/ssrn.3164764](https://doi.org/10.2139/ssrn.3164764)\n"
       ]
     },
     {

dev/_downloads/2e4791a177381a6102b21e44083615c8/plot_poisson_regression_non_normal_loss.ipynb

@@ -34,7 +34,7 @@
 
 .. [1]  A. Noll, R. Salzmann and M.V. Wuthrich, Case Study: French Motor
     Third-Party Liability Claims (November 8, 2018). `doi:10.2139/ssrn.3164764
-    <http://dx.doi.org/10.2139/ssrn.3164764>`_
+    <https://doi.org/10.2139/ssrn.3164764>`_
 """
 
 # Authors: Christian Lorentzen <[email protected]>

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Tweedie regression on insurance claims\n\nThis example illustrates the use of Poisson, Gamma and Tweedie regression on\nthe [French Motor Third-Party Liability Claims dataset](https://www.openml.org/d/41214), and is inspired by an R tutorial [1]_.\n\nIn this dataset, each sample corresponds to an insurance policy, i.e. a\ncontract within an insurance company and an individual (policyholder).\nAvailable features include driver age, vehicle age, vehicle power, etc.\n\nA few definitions: a *claim* is the request made by a policyholder to the\ninsurer to compensate for a loss covered by the insurance. The *claim amount*\nis the amount of money that the insurer must pay. The *exposure* is the\nduration of the insurance coverage of a given policy, in years.\n\nHere our goal is to predict the expected\nvalue, i.e. the mean, of the total claim amount per exposure unit also\nreferred to as the pure premium.\n\nThere are several possibilities to do that, two of which are:\n\n1. Model the number of claims with a Poisson distribution, and the average\n   claim amount per claim, also known as severity, as a Gamma distribution\n   and multiply the predictions of both in order to get the total claim\n   amount.\n2. Model the total claim amount per exposure directly, typically with a Tweedie\n   distribution of Tweedie power $p \\in (1, 2)$.\n\nIn this example we will illustrate both approaches. We start by defining a few\nhelper functions for loading the data and visualizing results.\n\n.. [1]  A. Noll, R. Salzmann and M.V. Wuthrich, Case Study: French Motor\n    Third-Party Liability Claims (November 8, 2018). [doi:10.2139/ssrn.3164764](http://dx.doi.org/10.2139/ssrn.3164764)\n"
+        "\n# Tweedie regression on insurance claims\n\nThis example illustrates the use of Poisson, Gamma and Tweedie regression on\nthe [French Motor Third-Party Liability Claims dataset](https://www.openml.org/d/41214), and is inspired by an R tutorial [1]_.\n\nIn this dataset, each sample corresponds to an insurance policy, i.e. a\ncontract within an insurance company and an individual (policyholder).\nAvailable features include driver age, vehicle age, vehicle power, etc.\n\nA few definitions: a *claim* is the request made by a policyholder to the\ninsurer to compensate for a loss covered by the insurance. The *claim amount*\nis the amount of money that the insurer must pay. The *exposure* is the\nduration of the insurance coverage of a given policy, in years.\n\nHere our goal is to predict the expected\nvalue, i.e. the mean, of the total claim amount per exposure unit also\nreferred to as the pure premium.\n\nThere are several possibilities to do that, two of which are:\n\n1. Model the number of claims with a Poisson distribution, and the average\n   claim amount per claim, also known as severity, as a Gamma distribution\n   and multiply the predictions of both in order to get the total claim\n   amount.\n2. Model the total claim amount per exposure directly, typically with a Tweedie\n   distribution of Tweedie power $p \\in (1, 2)$.\n\nIn this example we will illustrate both approaches. We start by defining a few\nhelper functions for loading the data and visualizing results.\n\n.. [1]  A. Noll, R. Salzmann and M.V. Wuthrich, Case Study: French Motor\n    Third-Party Liability Claims (November 8, 2018). [doi:10.2139/ssrn.3164764](https://doi.org/10.2139/ssrn.3164764)\n"
       ]
     },
     {

dev/_downloads/86c888008757148890daaf43d664fa71/plot_tweedie_regression_insurance_claims.py

@@ -32,7 +32,7 @@
 
 .. [1]  A. Noll, R. Salzmann and M.V. Wuthrich, Case Study: French Motor
     Third-Party Liability Claims (November 8, 2018). `doi:10.2139/ssrn.3164764
-    <http://dx.doi.org/10.2139/ssrn.3164764>`_
+    <https://doi.org/10.2139/ssrn.3164764>`_
 
 """