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[2.5] Update higgs data link #2945

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[2.5] Update higgs data link #2945

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2 changes: 1 addition & 1 deletion examples/advanced/random_forest/README.md
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Expand Up @@ -11,7 +11,7 @@ which is an optimized distributed gradient boosting library covering random fore
Follow along in this [notebook](./random_forest.ipynb) for an interactive experience.

### Dataset
This example illustrate a binary classification task based on [HIGGS dataset](https://archive.ics.uci.edu/dataset/280/higgs).
This example illustrate a binary classification task based on [HIGGS dataset](https://mlphysics.ics.uci.edu/data/higgs/).
This dataset contains 11 million instances, each with 28 attributes.

Please note that the UCI's website may experience occasional downtime.
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2 changes: 1 addition & 1 deletion examples/advanced/random_forest/random_forest.ipynb
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"which is an optimized distributed gradient boosting library, also covering random forest. In this example, we illustrate the use of NVFlare to carry out *horizontal* federated learning with tree-based collaboration - forming a random forest.\n",
"\n",
"### Dataset\n",
"This example illustrate a binary classification task based on [HIGGS dataset](https://archive.ics.uci.edu/dataset/280/higgs).\n",
"This example illustrate a binary classification task based on [HIGGS dataset](https://mlphysics.ics.uci.edu/data/higgs/).\n",
"This dataset contains 11 million instances, each with 28 attributes.\n",
"\n",
"### Horizontal Federated Learning\n",
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2 changes: 1 addition & 1 deletion examples/advanced/sklearn-linear/README.md
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Expand Up @@ -30,7 +30,7 @@ This can be achieved by setting the `warm_start` flag of SGDClassifier to
`True` in order to allow repeated fitting of the classifiers to the local data.

## Data preparation
The examples illustrate a binary classification task based on [HIGGS dataset](https://archive.ics.uci.edu/dataset/280/higgs).
The examples illustrate a binary classification task based on [HIGGS dataset](https://mlphysics.ics.uci.edu/data/higgs/).
This dataset contains 11 million instances, each with 28 attributes. Download the dataset from the HIGGS link above, containing a single `.csv` file.
By default, we assume the dataset is downloaded, uncompressed, and stored
in `DATASET_ROOT/HIGGS.csv`.
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2 changes: 1 addition & 1 deletion examples/advanced/sklearn-linear/sklearn_linear_higgs.ipynb
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"metadata": {},
"source": [
"## 2. Data preparation \n",
"The examples illustrate a binary classification task based on [HIGGS dataset](https://archive.ics.uci.edu/dataset/280/higgs).\n",
"The examples illustrate a binary classification task based on [HIGGS dataset](https://mlphysics.ics.uci.edu/data/higgs/).\n",
"This dataset contains 11 million instances, each with 28 attributes.\n",
"By default, we assume the dataset is downloaded, uncompressed, and stored in `DATASET_ROOT/HIGGS.csv`.\n",
"Please note that the UCI's website may experience occasional downtime.\n"
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2 changes: 1 addition & 1 deletion examples/advanced/vertical_xgboost/README.md
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Expand Up @@ -8,7 +8,7 @@ python3 -m pip install -r requirements.txt
```

## Preparing HIGGS Data
In this example we showcase a binary classification task based on the [HIGGS dataset](https://archive.ics.uci.edu/dataset/280/higgs), which contains 11 million instances, each with 28 features and 1 class label.
In this example we showcase a binary classification task based on the [HIGGS dataset](https://mlphysics.ics.uci.edu/data/higgs/), which contains 11 million instances, each with 28 features and 1 class label.

### Download and Store Dataset
First download the dataset from the HIGGS link above, which is a single zipped `.csv` file.
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2 changes: 1 addition & 1 deletion examples/advanced/xgboost/README.md
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Expand Up @@ -12,7 +12,7 @@ They use [XGBoost](https://github.com/dmlc/xgboost),
which is an optimized distributed gradient boosting library.

### HIGGS
The examples illustrate a binary classification task based on [HIGGS dataset](https://archive.ics.uci.edu/dataset/280/higgs).
The examples illustrate a binary classification task based on [HIGGS dataset](https://mlphysics.ics.uci.edu/data/higgs/).
This dataset contains 11 million instances, each with 28 attributes.

Please note that the UCI's website may experience occasional downtime.
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2 changes: 1 addition & 1 deletion examples/advanced/xgboost/data_job_setup.ipynb
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Expand Up @@ -21,7 +21,7 @@
"which is an optimized distributed gradient boosting library.\n",
"\n",
"### HIGGS\n",
"The examples illustrate a binary classification task based on [HIGGS dataset](https://archive.ics.uci.edu/dataset/280/higgs).\n",
"The examples illustrate a binary classification task based on [HIGGS dataset](https://mlphysics.ics.uci.edu/data/higgs/).\n",
"This dataset contains 11 million instances, each with 28 attributes.\n",
"Please note that the UCI's website may experience occasional downtime.\n",
"\n",
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4 changes: 2 additions & 2 deletions examples/hello-world/step-by-step/higgs/README.md
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@@ -1,8 +1,8 @@

# Training traditional ML classifiers with HIGGS dataset

The [HIGGS dataset](https://archive.ics.uci.edu/dataset/280/higgs) contains 11 million instances, each with 28 attributes, for binary classification to predict whether an event corresponds to the decayment of a Higgs boson or not. Follow the [prepare_data.ipynb](prepare_data.ipynb) notebook to download the HIGGS dataset and prepare the data splits.
(Please note that the [UCI's website](https://archive.ics.uci.edu/dataset/280/higgs) may experience occasional downtime)
The [HIGGS dataset](https://mlphysics.ics.uci.edu/data/higgs/) contains 11 million instances, each with 28 attributes, for binary classification to predict whether an event corresponds to the decayment of a Higgs boson or not. Follow the [prepare_data.ipynb](prepare_data.ipynb) notebook to download the HIGGS dataset and prepare the data splits.
(Please note that the [UCI's website](https://mlphysics.ics.uci.edu/data/higgs/) may experience occasional downtime)

The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator.
The data has been produced using Monte Carlo simulations. The first 21 features are kinematic properties measured by the particle detectors in the accelerator. The last 7 features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes.
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