Tree methods

In many datasets, the relationship between our inputs and output may not be a straight line. For example, consider the relationship between hour of the day and probability of posting on social media. If you were to draw a plot of this probability, it would likely increase during the evening and lunch break, and decline during the night, morning and workday, forming a sinusoidal wave pattern. A linear model cannot represent this kind of relationship, as the value of the response does not strictly increase or decrease with the hour of the day. What models, then, could we use to capture this relationship? In the specific case of time series models we could use approaches such as the Kalman filter described above, using the components of the structural time series equation to represent the cyclical 24-hour pattern of social media activity. In the following section we examine more general approaches that will apply both to time series data and to more general non-linear relationships...