Pushing the docs to dev/ for branch: master, commit f0da528b92ef72229e66e2c7fb779fe9324add90

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -16,7 +16,7 @@
 is the amount of money that the insurer must pay. The *exposure* is the
 duration of the insurance coverage of a given policy, in years.
 
-Here our goal goal is to predict the expected
+Here our goal is to predict the expected
 value, i.e. the mean, of the total claim amount per exposure unit also
 referred to as the pure premium.
 

dev/_downloads/86c888008757148890daaf43d664fa71/plot_tweedie_regression_insurance_claims.py

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Tweedie regression on insurance claims\n\n\nThis example illustrates the use of Poisson, Gamma and Tweedie regression on\nthe `French Motor Third-Party Liability Claims dataset\n<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 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\n    <http://dx.doi.org/10.2139/ssrn.3164764>`_\n"
+        "\n# Tweedie regression on insurance claims\n\n\nThis example illustrates the use of Poisson, Gamma and Tweedie regression on\nthe `French Motor Third-Party Liability Claims dataset\n<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\n    <http://dx.doi.org/10.2139/ssrn.3164764>`_\n"
       ]
     },
     {