When you create a new model and make predictions there are probably a few questions you have in mind. Namely, how accurate will the predictions be and what are the most important features? These are very important questions which will be answered with Terrene's built-in charts.
Accuracy and Loss
A prediction can only be as accurate as it's model. On Terrene there are two graphs which are automatically displayed on the workspace dashboard as soon as the model is trained, they are accuracy and loss. Both graphs show the change over time, so if you continue to train or add new data you can track the changes. Accuracy is a measure of how correct future predictions will be (measured as a percentage) and loss is a measure of how far off a prediction will be (measured in the same units as the prediction).
There are several ways to improve the accuracy of a model. Adding more data or more variables can often improve a models accuracy. Another option is to train the model more. By adding training time the model will become more accurate, there is a limit to this as eventually, the model will become as accurate as the data will allow, but, it is an easy way to improve initial results. More information on improving accuracy can be found here.
To determine which variables are important and which are not we need to look at the Feature Importance graph. This graph summarizes how much influence each variable has on the final outcome.
As you can see on this dataset (The Titanic dataset) age and gender were the two most important factors.