Understanding the training process
From the software engineer’s perspective, the training process is rather simple – we fit the model, validate it, and use it. We check how good the model is in terms of the performance metrics. If the model is good enough, and we can explain it, then we develop the entire product around it, or we use it in a larger software product.
When the model does not learn anything useful, we need to understand why this is the case and whether there could be another model that can. We can use the visualization techniques we learned about in Chapter 6 to explore the data and clear it from noise using the techniques from Chapter 4.
Now, let’s explore the process of how the decision tree model learns from the data. The DecisionTree classifier learns from the provided data by recursively partitioning the feature space based on the values of the features in the training dataset. It constructs a binary tree where each internal node represents...
