.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/linear_model/plot_tweedie_regression_insurance_claims.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py: ====================================== Tweedie regression on insurance claims ====================================== This example illustrates the use of Poisson, Gamma and Tweedie regression on the `French Motor Third-Party Liability Claims dataset `_, and is inspired by an R tutorial [1]_. In this dataset, each sample corresponds to an insurance policy, i.e. a contract within an insurance company and an individual (policyholder). Available features include driver age, vehicle age, vehicle power, etc. A few definitions: a *claim* is the request made by a policyholder to the insurer to compensate for a loss covered by the insurance. The *claim amount* 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 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. There are several possibilities to do that, two of which are: 1. Model the number of claims with a Poisson distribution, and the average claim amount per claim, also known as severity, as a Gamma distribution and multiply the predictions of both in order to get the total claim amount. 2. Model the total claim amount per exposure directly, typically with a Tweedie distribution of Tweedie power :math:`p \in (1, 2)`. In this example we will illustrate both approaches. We start by defining a few helper functions for loading the data and visualizing results. .. [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 `_ .. GENERATED FROM PYTHON SOURCE LINES 39-45 .. code-block:: Python # Authors: Christian Lorentzen # Roman Yurchak # Olivier Grisel # License: BSD 3 clause .. GENERATED FROM PYTHON SOURCE LINES 46-206 .. code-block:: Python from functools import partial import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn.datasets import fetch_openml from sklearn.metrics import ( mean_absolute_error, mean_squared_error, mean_tweedie_deviance, ) def load_mtpl2(n_samples=None): """Fetch the French Motor Third-Party Liability Claims dataset. Parameters ---------- n_samples: int, default=None number of samples to select (for faster run time). Full dataset has 678013 samples. """ # freMTPL2freq dataset from https://www.openml.org/d/41214 df_freq = fetch_openml(data_id=41214, as_frame=True).data df_freq["IDpol"] = df_freq["IDpol"].astype(int) df_freq.set_index("IDpol", inplace=True) # freMTPL2sev dataset from https://www.openml.org/d/41215 df_sev = fetch_openml(data_id=41215, as_frame=True).data # sum ClaimAmount over identical IDs df_sev = df_sev.groupby("IDpol").sum() df = df_freq.join(df_sev, how="left") df["ClaimAmount"].fillna(0, inplace=True) # unquote string fields for column_name in df.columns[df.dtypes.values == object]: df[column_name] = df[column_name].str.strip("'") return df.iloc[:n_samples] def plot_obs_pred( df, feature, weight, observed, predicted, y_label=None, title=None, ax=None, fill_legend=False, ): """Plot observed and predicted - aggregated per feature level. Parameters ---------- df : DataFrame input data feature: str a column name of df for the feature to be plotted weight : str column name of df with the values of weights or exposure observed : str a column name of df with the observed target predicted : DataFrame a dataframe, with the same index as df, with the predicted target fill_legend : bool, default=False whether to show fill_between legend """ # aggregate observed and predicted variables by feature level df_ = df.loc[:, [feature, weight]].copy() df_["observed"] = df[observed] * df[weight] df_["predicted"] = predicted * df[weight] df_ = ( df_.groupby([feature])[[weight, "observed", "predicted"]] .sum() .assign(observed=lambda x: x["observed"] / x[weight]) .assign(predicted=lambda x: x["predicted"] / x[weight]) ) ax = df_.loc[:, ["observed", "predicted"]].plot(style=".", ax=ax) y_max = df_.loc[:, ["observed", "predicted"]].values.max() * 0.8 p2 = ax.fill_between( df_.index, 0, y_max * df_[weight] / df_[weight].values.max(), color="g", alpha=0.1, ) if fill_legend: ax.legend([p2], ["{} distribution".format(feature)]) ax.set( ylabel=y_label if y_label is not None else None, title=title if title is not None else "Train: Observed vs Predicted", ) def score_estimator( estimator, X_train, X_test, df_train, df_test, target, weights, tweedie_powers=None, ): """Evaluate an estimator on train and test sets with different metrics""" metrics = [ ("D² explained", None), # Use default scorer if it exists ("mean abs. error", mean_absolute_error), ("mean squared error", mean_squared_error), ] if tweedie_powers: metrics += [ ( "mean Tweedie dev p={:.4f}".format(power), partial(mean_tweedie_deviance, power=power), ) for power in tweedie_powers ] res = [] for subset_label, X, df in [ ("train", X_train, df_train), ("test", X_test, df_test), ]: y, _weights = df[target], df[weights] for score_label, metric in metrics: if isinstance(estimator, tuple) and len(estimator) == 2: # Score the model consisting of the product of frequency and # severity models. est_freq, est_sev = estimator y_pred = est_freq.predict(X) * est_sev.predict(X) else: y_pred = estimator.predict(X) if metric is None: if not hasattr(estimator, "score"): continue score = estimator.score(X, y, sample_weight=_weights) else: score = metric(y, y_pred, sample_weight=_weights) res.append({"subset": subset_label, "metric": score_label, "score": score}) res = ( pd.DataFrame(res) .set_index(["metric", "subset"]) .score.unstack(-1) .round(4) .loc[:, ["train", "test"]] ) return res .. GENERATED FROM PYTHON SOURCE LINES 207-214 Loading datasets, basic feature extraction and target definitions ----------------------------------------------------------------- We construct the freMTPL2 dataset by joining the freMTPL2freq table, containing the number of claims (``ClaimNb``), with the freMTPL2sev table, containing the claim amount (``ClaimAmount``) for the same policy ids (``IDpol``). .. GENERATED FROM PYTHON SOURCE LINES 214-272 .. code-block:: Python from sklearn.compose import ColumnTransformer from sklearn.pipeline import make_pipeline from sklearn.preprocessing import ( FunctionTransformer, KBinsDiscretizer, OneHotEncoder, StandardScaler, ) df = load_mtpl2() # Correct for unreasonable observations (that might be data error) # and a few exceptionally large claim amounts df["ClaimNb"] = df["ClaimNb"].clip(upper=4) df["Exposure"] = df["Exposure"].clip(upper=1) df["ClaimAmount"] = df["ClaimAmount"].clip(upper=200000) # If the claim amount is 0, then we do not count it as a claim. The loss function # used by the severity model needs strictly positive claim amounts. This way # frequency and severity are more consistent with each other. df.loc[(df["ClaimAmount"] == 0) & (df["ClaimNb"] >= 1), "ClaimNb"] = 0 log_scale_transformer = make_pipeline( FunctionTransformer(func=np.log), StandardScaler() ) column_trans = ColumnTransformer( [ ( "binned_numeric", KBinsDiscretizer(n_bins=10, subsample=int(2e5), random_state=0), ["VehAge", "DrivAge"], ), ( "onehot_categorical", OneHotEncoder(), ["VehBrand", "VehPower", "VehGas", "Region", "Area"], ), ("passthrough_numeric", "passthrough", ["BonusMalus"]), ("log_scaled_numeric", log_scale_transformer, ["Density"]), ], remainder="drop", ) X = column_trans.fit_transform(df) # Insurances companies are interested in modeling the Pure Premium, that is # the expected total claim amount per unit of exposure for each policyholder # in their portfolio: df["PurePremium"] = df["ClaimAmount"] / df["Exposure"] # This can be indirectly approximated by a 2-step modeling: the product of the # Frequency times the average claim amount per claim: df["Frequency"] = df["ClaimNb"] / df["Exposure"] df["AvgClaimAmount"] = df["ClaimAmount"] / np.fmax(df["ClaimNb"], 1) with pd.option_context("display.max_columns", 15): print(df[df.ClaimAmount > 0].head()) .. rst-class:: sphx-glr-script-out .. code-block:: none ClaimNb Exposure Area VehPower VehAge DrivAge BonusMalus VehBrand \ IDpol 139 1 0.75 F 7 1 61 50 B12 190 1 0.14 B 12 5 50 60 B12 414 1 0.14 E 4 0 36 85 B12 424 2 0.62 F 10 0 51 100 B12 463 1 0.31 A 5 0 45 50 B12 VehGas Density Region ClaimAmount PurePremium Frequency \ IDpol 139 Regular 27000 R11 303.00 404.000000 1.333333 190 Diesel 56 R25 1981.84 14156.000000 7.142857 414 Regular 4792 R11 1456.55 10403.928571 7.142857 424 Regular 27000 R11 10834.00 17474.193548 3.225806 463 Regular 12 R73 3986.67 12860.225806 3.225806 AvgClaimAmount IDpol 139 303.00 190 1981.84 414 1456.55 424 5417.00 463 3986.67 .. GENERATED FROM PYTHON SOURCE LINES 273-282 Frequency model -- Poisson distribution --------------------------------------- The number of claims (``ClaimNb``) is a positive integer (0 included). Thus, this target can be modelled by a Poisson distribution. It is then assumed to be the number of discrete events occurring with a constant rate in a given time interval (``Exposure``, in units of years). Here we model the frequency ``y = ClaimNb / Exposure``, which is still a (scaled) Poisson distribution, and use ``Exposure`` as `sample_weight`. .. GENERATED FROM PYTHON SOURCE LINES 283-288 .. code-block:: Python from sklearn.linear_model import PoissonRegressor from sklearn.model_selection import train_test_split df_train, df_test, X_train, X_test = train_test_split(df, X, random_state=0) .. GENERATED FROM PYTHON SOURCE LINES 289-292 Let us keep in mind that despite the seemingly large number of data points in this dataset, the number of evaluation points where the claim amount is non-zero is quite small: .. GENERATED FROM PYTHON SOURCE LINES 293-295 .. code-block:: Python len(df_test) .. rst-class:: sphx-glr-script-out .. code-block:: none 169504 .. GENERATED FROM PYTHON SOURCE LINES 296-298 .. code-block:: Python len(df_test[df_test["ClaimAmount"] > 0]) .. rst-class:: sphx-glr-script-out .. code-block:: none 6237 .. GENERATED FROM PYTHON SOURCE LINES 299-306 As a consequence, we expect a significant variability in our evaluation upon random resampling of the train test split. The parameters of the model are estimated by minimizing the Poisson deviance on the training set via a Newton solver. Some of the features are collinear (e.g. because we did not drop any categorical level in the `OneHotEncoder`), we use a weak L2 penalization to avoid numerical issues. .. GENERATED FROM PYTHON SOURCE LINES 307-322 .. code-block:: Python glm_freq = PoissonRegressor(alpha=1e-4, solver="newton-cholesky") glm_freq.fit(X_train, df_train["Frequency"], sample_weight=df_train["Exposure"]) scores = score_estimator( glm_freq, X_train, X_test, df_train, df_test, target="Frequency", weights="Exposure", ) print("Evaluation of PoissonRegressor on target Frequency") print(scores) .. rst-class:: sphx-glr-script-out .. code-block:: none Evaluation of PoissonRegressor on target Frequency subset train test metric D² explained 0.0448 0.0427 mean abs. error 0.1379 0.1378 mean squared error 0.2441 0.2246 .. GENERATED FROM PYTHON SOURCE LINES 323-331 Note that the score measured on the test set is surprisingly better than on the training set. This might be specific to this random train-test split. Proper cross-validation could help us to assess the sampling variability of these results. We can visually compare observed and predicted values, aggregated by the drivers age (``DrivAge``), vehicle age (``VehAge``) and the insurance bonus/malus (``BonusMalus``). .. GENERATED FROM PYTHON SOURCE LINES 332-384 .. code-block:: Python fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(16, 8)) fig.subplots_adjust(hspace=0.3, wspace=0.2) plot_obs_pred( df=df_train, feature="DrivAge", weight="Exposure", observed="Frequency", predicted=glm_freq.predict(X_train), y_label="Claim Frequency", title="train data", ax=ax[0, 0], ) plot_obs_pred( df=df_test, feature="DrivAge", weight="Exposure", observed="Frequency", predicted=glm_freq.predict(X_test), y_label="Claim Frequency", title="test data", ax=ax[0, 1], fill_legend=True, ) plot_obs_pred( df=df_test, feature="VehAge", weight="Exposure", observed="Frequency", predicted=glm_freq.predict(X_test), y_label="Claim Frequency", title="test data", ax=ax[1, 0], fill_legend=True, ) plot_obs_pred( df=df_test, feature="BonusMalus", weight="Exposure", observed="Frequency", predicted=glm_freq.predict(X_test), y_label="Claim Frequency", title="test data", ax=ax[1, 1], fill_legend=True, ) .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_tweedie_regression_insurance_claims_001.png :alt: train data, test data, test data, test data :srcset: /auto_examples/linear_model/images/sphx_glr_plot_tweedie_regression_insurance_claims_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 385-402 According to the observed data, the frequency of accidents is higher for drivers younger than 30 years old, and is positively correlated with the `BonusMalus` variable. Our model is able to mostly correctly model this behaviour. Severity Model - Gamma distribution ------------------------------------ The mean claim amount or severity (`AvgClaimAmount`) can be empirically shown to follow approximately a Gamma distribution. We fit a GLM model for the severity with the same features as the frequency model. Note: - We filter out ``ClaimAmount == 0`` as the Gamma distribution has support on :math:`(0, \infty)`, not :math:`[0, \infty)`. - We use ``ClaimNb`` as `sample_weight` to account for policies that contain more than one claim. .. GENERATED FROM PYTHON SOURCE LINES 402-427 .. code-block:: Python from sklearn.linear_model import GammaRegressor mask_train = df_train["ClaimAmount"] > 0 mask_test = df_test["ClaimAmount"] > 0 glm_sev = GammaRegressor(alpha=10.0, solver="newton-cholesky") glm_sev.fit( X_train[mask_train.values], df_train.loc[mask_train, "AvgClaimAmount"], sample_weight=df_train.loc[mask_train, "ClaimNb"], ) scores = score_estimator( glm_sev, X_train[mask_train.values], X_test[mask_test.values], df_train[mask_train], df_test[mask_test], target="AvgClaimAmount", weights="ClaimNb", ) print("Evaluation of GammaRegressor on target AvgClaimAmount") print(scores) .. rst-class:: sphx-glr-script-out .. code-block:: none Evaluation of GammaRegressor on target AvgClaimAmount subset train test metric D² explained 3.900000e-03 4.400000e-03 mean abs. error 1.756746e+03 1.744042e+03 mean squared error 5.801770e+07 5.030677e+07 .. GENERATED FROM PYTHON SOURCE LINES 428-432 Those values of the metrics are not necessarily easy to interpret. It can be insightful to compare them with a model that does not use any input features and always predicts a constant value, i.e. the average claim amount, in the same setting: .. GENERATED FROM PYTHON SOURCE LINES 433-455 .. code-block:: Python from sklearn.dummy import DummyRegressor dummy_sev = DummyRegressor(strategy="mean") dummy_sev.fit( X_train[mask_train.values], df_train.loc[mask_train, "AvgClaimAmount"], sample_weight=df_train.loc[mask_train, "ClaimNb"], ) scores = score_estimator( dummy_sev, X_train[mask_train.values], X_test[mask_test.values], df_train[mask_train], df_test[mask_test], target="AvgClaimAmount", weights="ClaimNb", ) print("Evaluation of a mean predictor on target AvgClaimAmount") print(scores) .. rst-class:: sphx-glr-script-out .. code-block:: none Evaluation of a mean predictor on target AvgClaimAmount subset train test metric D² explained 0.000000e+00 -0.000000e+00 mean abs. error 1.756687e+03 1.744497e+03 mean squared error 5.803882e+07 5.033764e+07 .. GENERATED FROM PYTHON SOURCE LINES 456-465 We conclude that the claim amount is very challenging to predict. Still, the :class:`~sklearn.linear_model.GammaRegressor` is able to leverage some information from the input features to slightly improve upon the mean baseline in terms of D². Note that the resulting model is the average claim amount per claim. As such, it is conditional on having at least one claim, and cannot be used to predict the average claim amount per policy. For this, it needs to be combined with a claims frequency model. .. GENERATED FROM PYTHON SOURCE LINES 466-484 .. code-block:: Python print( "Mean AvgClaim Amount per policy: %.2f " % df_train["AvgClaimAmount"].mean() ) print( "Mean AvgClaim Amount | NbClaim > 0: %.2f" % df_train["AvgClaimAmount"][df_train["AvgClaimAmount"] > 0].mean() ) print( "Predicted Mean AvgClaim Amount | NbClaim > 0: %.2f" % glm_sev.predict(X_train).mean() ) print( "Predicted Mean AvgClaim Amount (dummy) | NbClaim > 0: %.2f" % dummy_sev.predict(X_train).mean() ) .. rst-class:: sphx-glr-script-out .. code-block:: none Mean AvgClaim Amount per policy: 71.78 Mean AvgClaim Amount | NbClaim > 0: 1951.21 Predicted Mean AvgClaim Amount | NbClaim > 0: 1940.95 Predicted Mean AvgClaim Amount (dummy) | NbClaim > 0: 1978.59 .. GENERATED FROM PYTHON SOURCE LINES 485-487 We can visually compare observed and predicted values, aggregated for the drivers age (``DrivAge``). .. GENERATED FROM PYTHON SOURCE LINES 487-514 .. code-block:: Python fig, ax = plt.subplots(ncols=1, nrows=2, figsize=(16, 6)) plot_obs_pred( df=df_train.loc[mask_train], feature="DrivAge", weight="Exposure", observed="AvgClaimAmount", predicted=glm_sev.predict(X_train[mask_train.values]), y_label="Average Claim Severity", title="train data", ax=ax[0], ) plot_obs_pred( df=df_test.loc[mask_test], feature="DrivAge", weight="Exposure", observed="AvgClaimAmount", predicted=glm_sev.predict(X_test[mask_test.values]), y_label="Average Claim Severity", title="test data", ax=ax[1], fill_legend=True, ) plt.tight_layout() .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_tweedie_regression_insurance_claims_002.png :alt: train data, test data :srcset: /auto_examples/linear_model/images/sphx_glr_plot_tweedie_regression_insurance_claims_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 515-545 Overall, the drivers age (``DrivAge``) has a weak impact on the claim severity, both in observed and predicted data. Pure Premium Modeling via a Product Model vs single TweedieRegressor -------------------------------------------------------------------- As mentioned in the introduction, the total claim amount per unit of exposure can be modeled as the product of the prediction of the frequency model by the prediction of the severity model. Alternatively, one can directly model the total loss with a unique Compound Poisson Gamma generalized linear model (with a log link function). This model is a special case of the Tweedie GLM with a "power" parameter :math:`p \in (1, 2)`. Here, we fix apriori the `power` parameter of the Tweedie model to some arbitrary value (1.9) in the valid range. Ideally one would select this value via grid-search by minimizing the negative log-likelihood of the Tweedie model, but unfortunately the current implementation does not allow for this (yet). We will compare the performance of both approaches. To quantify the performance of both models, one can compute the mean deviance of the train and test data assuming a Compound Poisson-Gamma distribution of the total claim amount. This is equivalent to a Tweedie distribution with a `power` parameter between 1 and 2. The :func:`sklearn.metrics.mean_tweedie_deviance` depends on a `power` parameter. As we do not know the true value of the `power` parameter, we here compute the mean deviances for a grid of possible values, and compare the models side by side, i.e. we compare them at identical values of `power`. Ideally, we hope that one model will be consistently better than the other, regardless of `power`. .. GENERATED FROM PYTHON SOURCE LINES 545-586 .. code-block:: Python from sklearn.linear_model import TweedieRegressor glm_pure_premium = TweedieRegressor(power=1.9, alpha=0.1, solver="newton-cholesky") glm_pure_premium.fit( X_train, df_train["PurePremium"], sample_weight=df_train["Exposure"] ) tweedie_powers = [1.5, 1.7, 1.8, 1.9, 1.99, 1.999, 1.9999] scores_product_model = score_estimator( (glm_freq, glm_sev), X_train, X_test, df_train, df_test, target="PurePremium", weights="Exposure", tweedie_powers=tweedie_powers, ) scores_glm_pure_premium = score_estimator( glm_pure_premium, X_train, X_test, df_train, df_test, target="PurePremium", weights="Exposure", tweedie_powers=tweedie_powers, ) scores = pd.concat( [scores_product_model, scores_glm_pure_premium], axis=1, sort=True, keys=("Product Model", "TweedieRegressor"), ) print("Evaluation of the Product Model and the Tweedie Regressor on target PurePremium") with pd.option_context("display.expand_frame_repr", False): print(scores) .. rst-class:: sphx-glr-script-out .. code-block:: none Evaluation of the Product Model and the Tweedie Regressor on target PurePremium Product Model TweedieRegressor subset train test train test metric D² explained NaN NaN 1.640000e-02 1.370000e-02 mean Tweedie dev p=1.5000 7.669930e+01 7.617050e+01 7.640770e+01 7.640880e+01 mean Tweedie dev p=1.7000 3.695740e+01 3.683980e+01 3.682880e+01 3.692270e+01 mean Tweedie dev p=1.8000 3.046010e+01 3.040530e+01 3.037600e+01 3.045390e+01 mean Tweedie dev p=1.9000 3.387580e+01 3.385000e+01 3.382120e+01 3.387830e+01 mean Tweedie dev p=1.9900 2.015716e+02 2.015414e+02 2.015347e+02 2.015587e+02 mean Tweedie dev p=1.9990 1.914573e+03 1.914370e+03 1.914538e+03 1.914387e+03 mean Tweedie dev p=1.9999 1.904751e+04 1.904556e+04 1.904747e+04 1.904558e+04 mean abs. error 2.730119e+02 2.722128e+02 2.739865e+02 2.731249e+02 mean squared error 3.295040e+07 3.212197e+07 3.295505e+07 3.213056e+07 .. GENERATED FROM PYTHON SOURCE LINES 587-595 In this example, both modeling approaches yield comparable performance metrics. For implementation reasons, the percentage of explained variance :math:`D^2` is not available for the product model. We can additionally validate these models by comparing observed and predicted total claim amount over the test and train subsets. We see that, on average, both model tend to underestimate the total claim (but this behavior depends on the amount of regularization). .. GENERATED FROM PYTHON SOURCE LINES 595-616 .. code-block:: Python res = [] for subset_label, X, df in [ ("train", X_train, df_train), ("test", X_test, df_test), ]: exposure = df["Exposure"].values res.append( { "subset": subset_label, "observed": df["ClaimAmount"].values.sum(), "predicted, frequency*severity model": np.sum( exposure * glm_freq.predict(X) * glm_sev.predict(X) ), "predicted, tweedie, power=%.2f" % glm_pure_premium.power: np.sum(exposure * glm_pure_premium.predict(X)), } ) print(pd.DataFrame(res).set_index("subset").T) .. rst-class:: sphx-glr-script-out .. code-block:: none subset train test observed 3.917618e+07 1.299546e+07 predicted, frequency*severity model 3.916555e+07 1.313276e+07 predicted, tweedie, power=1.90 3.951751e+07 1.325198e+07 .. GENERATED FROM PYTHON SOURCE LINES 617-646 Finally, we can compare the two models using a plot of cumulated claims: for each model, the policyholders are ranked from safest to riskiest based on the model predictions and the fraction of observed total cumulated claims is plotted on the y axis. This plot is often called the ordered Lorenz curve of the model. The Gini coefficient (based on the area between the curve and the diagonal) can be used as a model selection metric to quantify the ability of the model to rank policyholders. Note that this metric does not reflect the ability of the models to make accurate predictions in terms of absolute value of total claim amounts but only in terms of relative amounts as a ranking metric. The Gini coefficient is upper bounded by 1.0 but even an oracle model that ranks the policyholders by the observed claim amounts cannot reach a score of 1.0. We observe that both models are able to rank policyholders by risky-ness significantly better than chance although they are also both far from the oracle model due to the natural difficulty of the prediction problem from a few features: most accidents are not predictable and can be caused by environmental circumstances that are not described at all by the input features of the models. Note that the Gini index only characterizes the ranking performance of the model but not its calibration: any monotonic transformation of the predictions leaves the Gini index of the model unchanged. Finally one should highlight that the Compound Poisson Gamma model that is directly fit on the pure premium is operationally simpler to develop and maintain as it consists of a single scikit-learn estimator instead of a pair of models, each with its own set of hyperparameters. .. GENERATED FROM PYTHON SOURCE LINES 647-697 .. code-block:: Python from sklearn.metrics import auc def lorenz_curve(y_true, y_pred, exposure): y_true, y_pred = np.asarray(y_true), np.asarray(y_pred) exposure = np.asarray(exposure) # order samples by increasing predicted risk: ranking = np.argsort(y_pred) ranked_exposure = exposure[ranking] ranked_pure_premium = y_true[ranking] cumulated_claim_amount = np.cumsum(ranked_pure_premium * ranked_exposure) cumulated_claim_amount /= cumulated_claim_amount[-1] cumulated_samples = np.linspace(0, 1, len(cumulated_claim_amount)) return cumulated_samples, cumulated_claim_amount fig, ax = plt.subplots(figsize=(8, 8)) y_pred_product = glm_freq.predict(X_test) * glm_sev.predict(X_test) y_pred_total = glm_pure_premium.predict(X_test) for label, y_pred in [ ("Frequency * Severity model", y_pred_product), ("Compound Poisson Gamma", y_pred_total), ]: ordered_samples, cum_claims = lorenz_curve( df_test["PurePremium"], y_pred, df_test["Exposure"] ) gini = 1 - 2 * auc(ordered_samples, cum_claims) label += " (Gini index: {:.3f})".format(gini) ax.plot(ordered_samples, cum_claims, linestyle="-", label=label) # Oracle model: y_pred == y_test ordered_samples, cum_claims = lorenz_curve( df_test["PurePremium"], df_test["PurePremium"], df_test["Exposure"] ) gini = 1 - 2 * auc(ordered_samples, cum_claims) label = "Oracle (Gini index: {:.3f})".format(gini) ax.plot(ordered_samples, cum_claims, linestyle="-.", color="gray", label=label) # Random baseline ax.plot([0, 1], [0, 1], linestyle="--", color="black", label="Random baseline") ax.set( title="Lorenz Curves", xlabel="Fraction of policyholders\n(ordered by model from safest to riskiest)", ylabel="Fraction of total claim amount", ) ax.legend(loc="upper left") plt.plot() .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_tweedie_regression_insurance_claims_003.png :alt: Lorenz Curves :srcset: /auto_examples/linear_model/images/sphx_glr_plot_tweedie_regression_insurance_claims_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [] .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 8.139 seconds) .. _sphx_glr_download_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/linear_model/plot_tweedie_regression_insurance_claims.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_tweedie_regression_insurance_claims.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_tweedie_regression_insurance_claims.py ` .. include:: plot_tweedie_regression_insurance_claims.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_