Step 2.5: Choose a Model ML Universal Guides Google Developers

At this point, we have assembled our dataset and gained insights into the key characteristics of our data. Next, based on the metrics we gathered in Step 2, we should think about which classification model we should use. This means/ asking questions such as, "How do we present the text data to an algorithm that expects numeric input?" (this is called data preprocessing and vectorization), "What type of model should we use?", "What configuration parameters should we use for our model?", Thanks to decades of research, we have access to a large array of data preprocessing and model configuration options. However, the availability of a very large array of viable options to choose from greatly increases the complexity and the scope of the particular problem at hand.