In this particular case, in only 10 seconds and with the small dataset provided, the CLI tool was able to run quite a few iterations, meaning training multiple times based on different combinations of algorithms/configuration with different internal data transformations and algorithm's hyper-parameters. Finally, the "best quality" model found in 10 seconds is a model using a particular trainer/algorithm with any specific configuration. Depending on the exploration time, the command can produce a different result. The selection is based on the multiple metrics shown, such as Accuracy. The first and easiest metric to evaluate a binary-classification model is the accuracy, which is simple to understand. "Accuracy is the proportion of correct predictions with a test data set.".

Once the model is loaded, you can extract its input and output schema. The schemas are displayed for interest and learning only. The input schema is the fixed-length array of integer encoded words. The output schema is a float array of probabilities indicating whether a review's sentiment is negative, or positive . These values sum to 1, as the probability of being positive is the complement of the probability of the sentiment being negative.