update

Author: LittleLittleCloud

machine-learning/03-Training and AutoML.ipynb

@@ -29,9 +29,9 @@
         "\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 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"
+        "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` does is apply an algorithm to your data to identify patterns and estimate a function that lowers the loss. When you train a model, you want to decrease its loss to make the prediction of that model closer to the actual value."
+        "So what `Fit` does is to apply an algorithm to your data to identify patterns and adjust parameters in that algorithm to lowers the loss. When you train a model, you want to decrease its loss to make the prediction of that model closer to the actual value."
       ]
     },
     {