Update machine-learning/03-Training and AutoML.ipynb

Co-authored-by: Luis Quintanilla <46974588+luisquintanilla@users.noreply.github.com>

Author: LittleLittleCloud

machine-learning/03-Training and AutoML.ipynb

@@ -29,7 +29,7 @@
         "\n",
         "In ML.NET, you kick off a training job by calling the `Fit` method. So what happen when you call `Fit`? To answer that question you first need to define what a good model means. Generally, a good model means the values it predicts are very close to the actual value. The value to predict is commonly known as the **target** or **label**. For classification, a good model means it can classify data points correctly. For regression, a good model means its predicted numerical value is very close to actual numerical value. So we just need to find a way to quantify how close or how different the predicted and actual values are.\n",
         "\n",
-        "In machinelearning, such difference between predicted and actual label usually called `loss`, which measures distance between those values. There're difference losses for difference tasks, like softmax loss for classifiaction, rmse loss for regression. But in the gist they are all quantify metric to measure the distance between predicted and actual label. In most of cases, a lower `loss` means a better model.\n",
+        "In machine learning, the difference or distance between predicted and actual label is usually called **loss**. Similar to training algorithms, you use different loss measures based on the task. For classification softmax is a common loss measure. For regression, Root Mean Squared Error (RMSE) is a common loss measure. In general though, they are all metrics to quantify the distance between the predicted and actual value. In most of cases, a **lower loss means a better model**. For more information, see the [ML.NET evaluation metrics guide](https://docs.microsoft.com/dotnet/machine-learning/resources/metrics).\n"
         "\n",
         "So what `Fit(x)` does is to lower the `loss`. A good model means a model with small `loss`, when you train a model, you want to decrease its loss so as to make the prediction of that model closer to actual value. That's what __Train__ actually mean."
       ]