Merge branch 'u/xiaoyun/03' of https://github.com/LittleLittleCloud/csharp-notebooks into u/xiaoyun/03

Author: LittleLittleCloud

machine-learning/03-Training and AutoML.ipynb

@@ -39,7 +39,7 @@
       "metadata": {},
       "source": [
         "## Trainers in ML.Net\n",
-        "There're plenty of trainers available in ML.Net, where you can find most of them under [StandardTrainersCatalog](https://docs.microsoft.com/en-us/dotnet/api/microsoft.ml.standardtrainerscatalog?view=ml-dotnet). Among those trainers, there're simple, linear ones like `SDCA`, `Lbfgs`, `LinearSvm`, or tree-based non-linear trainer, like `FastTree`, `RandomForest` and `LightGbm`. Generally, each trainer's capability is difference and non-linear models have a better training performance(lower loss) than linear ones, but it doesn't always mean they are always the better choice. It's an open question in picking trainers and usually requires many attempts to find out the best one.\n",
+        "ML.NET provides a variety of trainers. You can find most of them under the [StandardTrainersCatalog](https://docs.microsoft.com/dotnet/api/microsoft.ml.standardtrainerscatalog?view=ml-dotnet). Examples of trainers include linear trainers like `SDCA`, `Lbfgs`, `LinearSvm` and tree-based non-linear trainerd like `FastTree`, `RandomForest` and `LightGbm`. Generally, each trainer's capability is different. Non-linear models sometimes have better training performance (lower loss) than linear ones, but it doesn't always mean they are always the better choice. Picking the right trainer to build the best model for your data requires many attempts of trial and error.\n",
         "\n",
         "### Hyper-parameter optimization\n",
         "Other than difference in trainers, different hyper-parameter in one trainer also have a huge impact over the final training performance, especially for tree-base trainers. This is because the capability of these trainers to fit a specific dataset is largly depends on their hyper parameters. For example, larger `numberOfLeaves` in `LightGbm` results to a larger model and usually enable it to fit on a more complex dataset, but it might have countereffect on small dataset and cause overfitting. On the contrary, if the dataset is complex but you set a small `numberOfLeaves`, it might impair `LightGbm`'s ability on fitting that dataset and cause underfit.\n",