diff --git a/docs/assets/pydvl.bib b/docs/assets/pydvl.bib
index aa6a206e6..c622e5541 100644
--- a/docs/assets/pydvl.bib
+++ b/docs/assets/pydvl.bib
@@ -312,3 +312,19 @@ @inproceedings{yan_if_2021
   langid = {english},
   keywords = {notion}
 }
+
+@InProceedings{kwon_data_2023,
+  title = 	 {Data-{OOB}: Out-of-bag Estimate as a Simple and Efficient Data Value},
+  author =       {Kwon, Yongchan and Zou, James},
+  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
+  pages = 	 {18135--18152},
+  year = 	 {2023},
+  editor = 	 {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan},
+  volume = 	 {202},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {23--29 Jul},
+  publisher =    {PMLR},
+  pdf = 	 {https://proceedings.mlr.press/v202/kwon23e/kwon23e.pdf},
+  url = 	 {https://proceedings.mlr.press/v202/kwon23e.html},
+  abstract = 	 {Data valuation is a powerful framework for providing statistical insights into which data are beneficial or detrimental to model training. Many Shapley-based data valuation methods have shown promising results in various downstream tasks, however, they are well known to be computationally challenging as it requires training a large number of models. As a result, it has been recognized as infeasible to apply to large datasets. To address this issue, we propose Data-OOB, a new data valuation method for a bagging model that utilizes the out-of-bag estimate. The proposed method is computationally efficient and can scale to millions of data by reusing trained weak learners. Specifically, Data-OOB takes less than $2.25$ hours on a single CPU processor when there are $10^6$ samples to evaluate and the input dimension is $100$. Furthermore, Data-OOB has solid theoretical interpretations in that it identifies the same important data point as the infinitesimal jackknife influence function when two different points are compared. We conduct comprehensive experiments using 12 classification datasets, each with thousands of sample sizes. We demonstrate that the proposed method significantly outperforms existing state-of-the-art data valuation methods in identifying mislabeled data and finding a set of helpful (or harmful) data points, highlighting the potential for applying data values in real-world applications.}
+}
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 51f24e756..7ba274e51 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -199,6 +199,7 @@ nav:
       - KNN Shapley: examples/shapley_knn_flowers.ipynb
       - Data utility learning: examples/shapley_utility_learning.ipynb
       - Least Core: examples/least_core_basic.ipynb
+      - Data OOB: examples/data_oob.ipynb
   - The Influence Function:
     - Introduction: influence/index.md
     - Examples:
diff --git a/notebooks/data_oob.ipynb b/notebooks/data_oob.ipynb
new file mode 100644
index 000000000..8891a1ad1
--- /dev/null
+++ b/notebooks/data_oob.ipynb
@@ -0,0 +1,483 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bagging for data valuation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook introduces the Data-OOB method, an implementation based on a publication from Kwon and Zou \"[Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value](https://proceedings.mlr.press/v202/kwon23e.html)\" ICML 2023 , using pyDVL.\n",
+    "\n",
+    "\n",
+    "The objective of this paper is mainly to overcome the computational bottleneck of shapley-based data valuation methods that require to fit a significant number of models to accurately estimate marginal contributions.\n",
+    "The algorithms computes data values from out of bag estimates using a bagging model.\n",
+    "\n",
+    "The value can be interpreted as a partition of the OOB estimate, which is originally introduced to estimate the prediction error. This OOB estimate is given as:\n",
+    "\n",
+    "$$\n",
+    "\\sum_{i=1}^n\\frac{\\sum_{b=1}^{B}\\mathbb{1}(w_{bi}=0)T(y_i, \\hat{f}_b(x_i))}{\\sum_{b=1}^{B}\n",
+    "\\mathbb{1}\n",
+    "(w_{bi}=0)}\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "We begin by importing the main libraries and setting some defaults.\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "If you are reading this in the documentation, some boilerplate (including most plotting code) has been omitted for convenience.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from pydvl.value import compute_data_oob\n",
+    "from pydvl.utils import Dataset, Utility, Scorer\n",
+    "from pydvl.reporting.scores import compute_removal_score\n",
+    "from pydvl.reporting.plots import shaded_mean_std\n",
+    "from pydvl.value.result import ValuationResult"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   age  fnlwgt  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
+      "0   39   77516             13          2174             0              40   \n",
+      "1   50   83311             13             0             0              13   \n",
+      "2   38  215646              9             0             0              40   \n",
+      "3   53  234721              7             0             0              40   \n",
+      "4   28  338409             13             0             0              40   \n",
+      "\n",
+      "  income  \n",
+      "0  <=50K  \n",
+      "1  <=50K  \n",
+      "2  <=50K  \n",
+      "3  <=50K  \n",
+      "4  <=50K  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Specify the URL of the dataset\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\"\n",
+    "\n",
+    "# Specify the column names\n",
+    "column_names = [\n",
+    "    \"age\",\n",
+    "    \"workclass\",\n",
+    "    \"fnlwgt\",\n",
+    "    \"education\",\n",
+    "    \"education-num\",\n",
+    "    \"marital-status\",\n",
+    "    \"occupation\",\n",
+    "    \"relationship\",\n",
+    "    \"race\",\n",
+    "    \"sex\",\n",
+    "    \"capital-gain\",\n",
+    "    \"capital-loss\",\n",
+    "    \"hours-per-week\",\n",
+    "    \"native-country\",\n",
+    "    \"income\",\n",
+    "]\n",
+    "\n",
+    "# Specify the data types for each column\n",
+    "data_types = {\n",
+    "    \"age\": int,\n",
+    "    \"workclass\": \"category\",\n",
+    "    \"fnlwgt\": int,\n",
+    "    \"education\": \"category\",\n",
+    "    \"education-num\": int,\n",
+    "    \"marital-status\": \"category\",\n",
+    "    \"occupation\": \"category\",\n",
+    "    \"relationship\": \"category\",\n",
+    "    \"race\": \"category\",\n",
+    "    \"sex\": \"category\",\n",
+    "    \"capital-gain\": int,\n",
+    "    \"capital-loss\": int,\n",
+    "    \"hours-per-week\": int,\n",
+    "    \"native-country\": \"category\",\n",
+    "    \"income\": \"category\",\n",
+    "}\n",
+    "\n",
+    "# Load the dataset into a pandas DataFrame\n",
+    "data_adult = pd.read_csv(\n",
+    "    url,\n",
+    "    names=column_names,\n",
+    "    sep=\",\\s*\",\n",
+    "    engine=\"python\",\n",
+    "    na_values=\"?\",\n",
+    "    dtype=data_types,\n",
+    "    nrows=2000,\n",
+    ")\n",
+    "\n",
+    "# Drop categorical columns\n",
+    "data_adult = data_adult.drop(\n",
+    "    columns=[\n",
+    "        \"workclass\",\n",
+    "        \"education\",\n",
+    "        \"marital-status\",\n",
+    "        \"occupation\",\n",
+    "        \"relationship\",\n",
+    "        \"race\",\n",
+    "        \"sex\",\n",
+    "        \"native-country\",\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Display the first few rows of the dataframe\n",
+    "print(data_adult.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "data = Dataset.from_arrays(\n",
+    "    X=data_adult.drop(columns=[\"income\"]).values,\n",
+    "    y=data_adult.loc[:, \"income\"].cat.codes.values,\n",
+    ")\n",
+    "\n",
+    "model = KNeighborsClassifier(n_neighbors=5)\n",
+    "\n",
+    "utility = Utility(model, data, Scorer(\"accuracy\", default=0.0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oob_values = compute_data_oob(utility, n_est=1000, max_samples=0.95)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "oob_values.sort(key=\"value\")\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15, 6])\n",
+    "\n",
+    "ax.plot(np.arange(len(oob_values.values)), oob_values.values)\n",
+    "ax.set_title(\"Data-OOB values\")\n",
+    "ax.set_ylabel(\"Data-oob value\")\n",
+    "ax.set_xlabel(\"Point rank\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15, 6])\n",
+    "ax.hist(oob_values.values, bins=50)\n",
+    "ax.set_title(\"Histogram of Data-OOB values\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variance\n",
+    "\n",
+    "The variance it the weak learner variance. It is computed with Welford's online algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "plot_list = [(a, b) for a, b in zip(oob_values.values, oob_values.variances)]\n",
+    "plot_list = pd.DataFrame(plot_list).sample(100).sort_values(by=0).values\n",
+    "yerr = [x[1] ** 2 for x in plot_list]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15, 10])\n",
+    "ax.errorbar(\n",
+    "    x=np.arange(len(yerr)), y=[x[0] for x in plot_list], yerr=yerr, fmt=\".\", capsize=6\n",
+    ")\n",
+    "plt.title(\"Data-OOB values\")\n",
+    "ax.set_ylabel(\"Data-oob value\")\n",
+    "ax.set_xlabel(\"Point rank\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Point removal experiments\n",
+    "\n",
+    "The standard procedure for the evaluation of data valuation schemes is the point removal experiment. The objective is to measure the evolution of performance when the best/worst points are removed from the training set. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "mean_colors = [\"dodgerblue\", \"indianred\", \"limegreen\", \"darkorange\", \"darkorchid\"]\n",
+    "shade_colors = [\"lightskyblue\", \"firebrick\", \"seagreen\", \"gold\", \"plum\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "all_best_scores = []\n",
+    "all_worst_scores = []\n",
+    "\n",
+    "removal_percentages = np.arange(0, 0.99, 0.01)\n",
+    "\n",
+    "for i in range(5):\n",
+    "    for method_name in [\"Random\", \"oob\"]:\n",
+    "        if method_name == \"Random\":\n",
+    "            values = ValuationResult.from_random(size=len(utility.data))\n",
+    "        else:\n",
+    "            values = compute_data_oob(utility, n_est=300, max_samples=0.95)\n",
+    "\n",
+    "        best_scores = compute_removal_score(\n",
+    "            u=utility,\n",
+    "            values=values,\n",
+    "            percentages=removal_percentages,\n",
+    "            remove_best=True,\n",
+    "        )\n",
+    "        best_scores[\"method_name\"] = method_name\n",
+    "        all_best_scores.append(best_scores)\n",
+    "\n",
+    "        worst_scores = compute_removal_score(\n",
+    "            u=utility,\n",
+    "            values=values,\n",
+    "            percentages=removal_percentages,\n",
+    "            remove_best=False,\n",
+    "        )\n",
+    "        worst_scores[\"method_name\"] = method_name\n",
+    "        all_worst_scores.append(worst_scores)\n",
+    "\n",
+    "best_scores_df = pd.DataFrame(all_best_scores)\n",
+    "worst_scores_df = pd.DataFrame(all_worst_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n",
+    "\n",
+    "for i, method_name in enumerate([\"Random\", \"oob\"]):\n",
+    "    shaded_mean_std(\n",
+    "        best_scores_df[best_scores_df[\"method_name\"] == method_name].drop(\n",
+    "            columns=[\"method_name\"]\n",
+    "        ),\n",
+    "        abscissa=removal_percentages,\n",
+    "        mean_color=mean_colors[i],\n",
+    "        shade_color=shade_colors[i],\n",
+    "        xlabel=\"Percentage removed\",\n",
+    "        ylabel=utility.scorer._name.capitalize(),\n",
+    "        label=method_name,\n",
+    "        title=\"Accuracy as a function of percentage of removed best data points\",\n",
+    "        ax=ax[1],\n",
+    "    )\n",
+    "    shaded_mean_std(\n",
+    "        worst_scores_df[worst_scores_df[\"method_name\"] == method_name].drop(\n",
+    "            columns=[\"method_name\"]\n",
+    "        ),\n",
+    "        abscissa=removal_percentages,\n",
+    "        mean_color=mean_colors[i],\n",
+    "        shade_color=shade_colors[i],\n",
+    "        xlabel=\"Percentage removed\",\n",
+    "        ylabel=utility.scorer._name.capitalize(),\n",
+    "        label=method_name,\n",
+    "        title=\"Accuracy as a function of percentage of removed worst data points\",\n",
+    "        ax=ax[0],\n",
+    "    )\n",
+    "ax[0].legend()\n",
+    "ax[1].legend()\n",
+    "plt.show();"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Edit Metadata",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4e000971326892723e7f31ded70802f690c31c3620f59a0f99e594aaee3047ef"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/pydvl/value/__init__.py b/src/pydvl/value/__init__.py
index f49d7bc73..6cfcc2160 100644
--- a/src/pydvl/value/__init__.py
+++ b/src/pydvl/value/__init__.py
@@ -10,6 +10,7 @@
 from ..utils import Dataset, Scorer, Utility
 from .least_core import *
 from .loo import *
+from .oob import *
 from .sampler import *
 from .semivalues import *
 from .shapley import *
diff --git a/src/pydvl/value/oob/__init__.py b/src/pydvl/value/oob/__init__.py
new file mode 100644
index 000000000..bbf1a15c1
--- /dev/null
+++ b/src/pydvl/value/oob/__init__.py
@@ -0,0 +1 @@
+from .oob import *
diff --git a/src/pydvl/value/oob/oob.py b/src/pydvl/value/oob/oob.py
new file mode 100644
index 000000000..c62a6255d
--- /dev/null
+++ b/src/pydvl/value/oob/oob.py
@@ -0,0 +1,140 @@
+"""
+## References
+
+[^1]: <a name="kwon_data_2023"></a>Kwon et al.
+[Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value](https://proceedings.mlr.press/v202/kwon23e.html).
+In: Published at ICML 2023
+
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import TypeVar
+
+import numpy as np
+from numpy.typing import NDArray
+from sklearn.base import is_classifier, is_regressor
+from sklearn.ensemble import BaggingClassifier, BaggingRegressor
+
+from pydvl.utils import Utility, maybe_progress
+from pydvl.value.result import ValuationResult
+
+__all__ = ["compute_data_oob"]
+
+T = TypeVar("T", bound=np.number)
+
+
+def compute_data_oob(
+    u: Utility,
+    n_est: int = 10,
+    max_samples: float = 0.8,
+    n_jobs: int = None,
+    loss: Callable = None,
+    *,
+    progress: bool = False,
+) -> ValuationResult:
+    r"""Computes Data out of bag values
+
+    This implements the method described in (Kwon and Zou, 2023) <sup><a href="kwon_data_2023">1</a></sup>.
+    It fits several base estimators provided through u.model through a bagging process. The point value corresponds to the average loss of estimators which were not fit on it.
+
+    $w_{bj}\in Z$ is the number of times the j-th datum $(x_j, y_j)$ is selected in the b-th bootstrap dataset.
+
+    $$\psi((x_i,y_i),\Theta_B):=\frac{\sum_{b=1}^{B}\mathbb{1}(w_{bi}=0)T(y_i, \hat{f}_b(x_i))}{\sum_{b=1}^{B}
+    \mathbb{1}
+    (w_{bi}=0)}$$
+
+    With:
+
+    $$
+    T: Y \times Y
+    \rightarrow \mathbb{R}
+    $$
+
+    T is a score function that represents the goodness of a weak learner $\hat{f}_b$ at the i-th datum $(x_i, y_i)$.
+
+    There is a need to tune n_est and max_samples jointly to ensure all samples are at least 1 time oob, otherwise the result could include a nan value for that datum.
+
+    Args:
+        u: Utility object with model, data, and scoring function.
+        n_est: Number of estimator used in the bagging procedure.
+        max_samples: The fraction of samples to draw to train each base estimator.
+        n_jobs: The number of jobs to run in parallel used in the bagging
+            procedure for both fit and predict.
+        loss: A function taking as parameters model prediction and corresponding
+            data labels(preds, y) and returning an array of point-wise errors.
+        progress: If True, display a progress bar.
+
+    Returns:
+        Object with the data values.
+    """
+
+    result: ValuationResult[np.int_, np.float_] = ValuationResult.empty(
+        algorithm="data_oob", indices=u.data.indices, data_names=u.data.data_names
+    )
+
+    if is_classifier(u.model):
+        bag = BaggingClassifier(
+            u.model, n_estimators=n_est, max_samples=max_samples, n_jobs=n_jobs
+        )
+        if loss is None:
+            loss = point_wise_accuracy
+    elif is_regressor(u.model):
+        bag = BaggingRegressor(
+            u.model, n_estimators=n_est, max_samples=max_samples, n_jobs=n_jobs
+        )
+        if loss is None:
+            loss = neg_l2_distance
+    else:
+        raise Exception(
+            "Model has to be a classifier or a regressor in sklearn format."
+        )
+
+    bag.fit(u.data.x_train, u.data.y_train)
+
+    for est, samples in maybe_progress(
+        zip(bag.estimators_, bag.estimators_samples_), progress, total=n_est
+    ):  # The bottleneck is the bag fitting not this part so TQDM is not very useful here
+        oob_idx = np.setxor1d(u.data.indices, np.unique(samples))
+        array_loss = loss(
+            preds=est.predict(u.data.x_train[oob_idx]), y=u.data.y_train[oob_idx]
+        )
+        result += ValuationResult(
+            algorithm="data_oob",
+            indices=oob_idx,
+            values=array_loss,
+            counts=np.ones_like(array_loss, dtype=u.data.indices.dtype),
+        )
+    return result
+
+
+def point_wise_accuracy(preds: NDArray, y: NDArray) -> NDArray:
+    r"""Computes point wise accuracy
+
+    Args:
+        preds: Model prediction on
+        y:  data labels corresponding to the model predictions
+
+    Returns:
+        Array of point wise accuracy
+    """
+    return np.array(preds == y, dtype=np.int_)
+
+
+def neg_l2_distance(preds: NDArray[T], y: NDArray[T]) -> NDArray[T]:
+    r"""Computes negative l2 distance between label and model prediction
+
+    Args:
+        preds: Model prediction on
+        y:  data labels corresponding to the model predictions
+
+    Returns:
+        Array with point wise negative l2 distance between label and model prediction
+    """
+    return -np.square(
+        np.array(
+            preds - y,
+            dtype=np.float64,
+        )
+    )