Linearity: Many machine learning algorithms assume the input data is linear meaning these models will assume data classifications can be separated along a straight line or that the data follows a linear trend. Training Time: The amount of training time needed varies between machine learning algorithms and can also vary by the desired level of accuracy. The following are a few considerations to take into account when beginning a machine learning project:Īccuracy: Is the goal of your project to determine the most accurate result or will an approximation satisfy your project needs? Approximating outputs can reduce processing time and keep performance high for large datasets. Choosing the proper machine learning algorithm lends context to the insights gained from the resulting predictions. The machine learning algorithm you choose depends on the size, quality, and type of data as well as the project timeline and your overall goals. Machine Learning: Choosing a Machine Learning Model As more companies look to leverage their data using the predictive capabilities of machine learning, they find that there is no one size fits all approach to this exciting technology.
0 Comments
Leave a Reply. |