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📗 Fix docs (#83)
* 📗 Add links in Readme * 📁 Small fixes in notebooks * 📗 Add links in tutorial page * 📗 Fix changelog
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Readme.rst

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**Train and predict uplift model**
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Use the intuitive python API to train uplift models with `sklift.models <https://www.uplift-modeling.com/en/latest/api/models/index.html>`__.
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.. code-block:: python
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# import approaches
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**Evaluate your uplift model**
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Uplift model evaluation metrics are available in `sklift.metrics <https://www.uplift-modeling.com/en/latest/api/metrics/index.html>`__.
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.. code-block:: python
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# import metrics to evaluate your model
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**Vizualize the results**
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Visualize performance metrics with `sklift.viz <https://www.uplift-modeling.com/en/latest/api/viz/index.html>`__.
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.. code-block:: python
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# import vizualisation tools

docs/changelog.md

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* 🔨 something that previously didn’t work as documentated – or according to reasonable expectations – should now work.
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* ❗️ you will need to change your code to have the same effect in the future; or a feature will be removed in the future.
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## Version 0.3.1
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### [sklift.datasets](https://www.uplift-modeling.com/en/v0.3.1/api/datasets/index.html)
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* 🔨 Fix bugs in [sklift.datasets](https://www.uplift-modeling.com/en/v0.3.1/api/datasets/index.html)
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### [sklift.metrics](https://www.uplift-modeling.com/en/v0.3.1/api/index/metrics.html)
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* 📝 Imporve [uplift_by_percentile](https://www.uplift-modeling.com/en/v0.3.1/api/metrics/uplift_by_percentile.html) function by [@ElisovaIra](https://github.com/ElisovaIra).
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### Miscellaneous
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* 💥 Add tutorial ["Uplift modeling metrics"](https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/uplift_metrics_tutorial.ipynb) by [@ElisovaIra](https://github.com/ElisovaIra).
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## Version 0.3.0
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### [sklift.datasets](https://www.uplift-modeling.com/en/v0.3.0/api/datasets/index.html)

docs/tutorials.rst

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- `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__
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- `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__
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`Uplift modeling metrics`_
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----------------------------------------------------------------------------------
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.. list-table::
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:align: center
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:widths: 12 15 10 8
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* - In English 🇬🇧
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- |Open In Colab1|
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- `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/uplift_metrics_tutorial.ipynb>`__
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- `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/uplift_metrics_tutorial.ipynb>`__
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`Example of usage model from sklift.models in sklearn.pipeline`_
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notebooks/Readme.rst

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- `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__
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- `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__
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`Uplift modeling metrics`_
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----------------------------------------------------------------------------------
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.. list-table::
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:align: center
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:widths: 12 15 10 8
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* - In English 🇬🇧
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- |Open In Colab1|
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- `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/uplift_metrics_tutorial.ipynb>`__
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- `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/uplift_metrics_tutorial.ipynb>`__
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`Example of usage model from sklift.models in sklearn.pipeline`_
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----------------------------------------------------------------------------------
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notebooks/pipeline_usage_RU.ipynb

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},
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"outputs": [],
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"source": [
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"# !pip install scikit-uplift xgboost==1.0.2 category_encoders==2.1.0 -U"
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"# pip install scikit-uplift xgboost==1.0.2 category_encoders==2.1.0 -U"
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]
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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},
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"pycharm": {
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"stem_cell": {
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"cell_type": "raw",
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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}

notebooks/uplift_metrics_tutorial.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 🎯 Uplift modeling `metrics`"
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"# 🎯 Uplift modeling `metrics`\n",
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"\n",
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"<br>\n",
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"<center>\n",
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" <a href=\"https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/uplift_metrics_tutorial.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\">\n",
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" </a>\n",
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" <br>\n",
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" <b><a href=\"https://github.com/maks-sh/scikit-uplift/\">SCIKIT-UPLIFT REPO</a> | </b>\n",
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" <b><a href=\"https://scikit-uplift.readthedocs.io/en/latest/\">SCIKIT-UPLIFT DOCS</a> | </b>\n",
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" <b><a href=\"https://scikit-uplift.readthedocs.io/en/latest/user_guide/index.html\">USER GUIDE</a></b>\n",
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" <br>\n",
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"</center>"
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{
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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},
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"pycharm": {
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"stem_cell": {
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"cell_type": "raw",
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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"nbformat": 4,
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}

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