Underfitting
Consider an alternative situation in which the data has 10 features, but you only make use of 1 feature. Your model hypothesis would still be the following:
![Figure 7.4: Equation for a hypothesis for a line](https://static.packt-cdn.com/products/9781800566927/graphics/image/B15019_07_04.jpg)
Figure 7.4: Equation for a hypothesis for a line
However, that is the equation of a straight line, but your model is probably ignoring a lot of information. The model is over-simplified and is said to underfit the data.
The solution to underfitting is to provide the model with more features, or conversely, less data to train on; but more features is the better approach.