doc: add request links for ordinal loss implementation in gradient bo…

…osting frameworks

docs/motivation.ipynb

@@ -9,7 +9,7 @@
  "\n",
  "Usually when faced with prediction problems involving ordered labels (i.e. low, medium, high) and tabular data, data scientists turn to regular multinomial classifiers from the gradient boosted tree family of models, because of their ease of use, speed of fitting, and good performance. Parametric ordinal models have been around for a while, but they have not been popular because of their poor performance compared to the gradient boosted models, especially for larger datasets.\n",
  "\n",
- "Although classifiers can predict ordinal labels adequately, they require building as many classifiers as there are labels to predict. This approach, however, leads to slower training times, and confusing feature interpretations. For example, a feature which is positively associated with the increasing order of the label set (i.e. as the feature's value grows, so do the probabilities of the higher ordered labels), will va a positive association with the highest ordered label, negative with the lowest ordered, and a \"concave\" association with the middle ones."
+ "Although classifiers can predict ordinal labels adequately, they require building as many classifiers as there are labels to predict. This approach, however, leads to slower training times, and confusing feature interpretations. For example, a feature which is positively associated with the increasing order of the label set (i.e. as the feature's value grows, so do the probabilities of the higher ordered labels), will va a positive association with the highest ordered label, negative with the lowest ordered, and a \"concave\" association with the middle ones.\n"
  ]
  },
  {
@@ -33,6 +33,14 @@
  "</div>"
  ]
  },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "There's been recurring requests from the community for an ordinal loss implementation in all of the major gradient boosting model frameworks ([LightGBM](https://github.com/microsoft/LightGBM/issues/5882), [XGBoost](https://github.com/dmlc/xgboost/issues/5243), [XGBoost](https://github.com/dmlc/xgboost/issues/695), [CatBoost](https://github.com/catboost/catboost/issues/1994))."
+ ]
+ },
  {
  "cell_type": "markdown",
  "metadata": {