Deciding on the right Machine Learning Algorithm for a business problem or a use case is a complicated and time taking process. If you apply any ML model to the business use case and test if the model is the right fit, then you are following a tedious approach, one that is time-consuming and requires a lot of effort. There are certain factors you should consider when choosing the right ML algorithm that best suits your business requirement and objectives.
In this blog, we will explore the various factors that play a role in refining your selection for the correct ML Model.
It is a best practice to understand the type of business problem at hand and select the algorithm that helps to best solve it. You can categorize the problem based on the input and output. Based on the input, you can categorize the business problem into three types
- Supervised learning problem – If it involves labeled data
- Unsupervised learning problem – If unlabeled data is involved
- Reinforcement learning problem – If it involves optimizing an objective by interacting with the environment
Based on the output, you can categorize the problem into three types:
- Regression problem if the output is a number
- Classification problem if the output of the ML model is a class
- Clustering problem if the output is a set of input groups
Training Set Size
Data sets act as the raw material for the entire analysis process and play a bigger role in the selection of the algorithms. For small training data sets, high bias or low variance classifiers work the best and as for large training data sets, low bias or high variance classifiers help to build highly accurate models.
The required accuracy depends on the type of application built. Some applications require an approximate prediction which in turn helps to reduce processing time. Flexible models are preferred if the objective of the business problem is high accuracy and if inference is the goal, then restrictive models would suffice.
The type of use case determines the training period of the model. For certain use cases like movie recommendations, the model will be trained every time the user logs in, and for something like stock predictions, the model has to be trained every second. Hence it becomes imperative to consider the time taken to train the model.
Linear algorithms are simple and fast to train and hence are mostly used as the first line of attack. Most of the ML models like Support Vector Machines, Logistic Regression, Linear aggression, and others leverage linearity. Even though it helps to solve some business problems, in some cases, it may bring the accuracy down.
Number of Parameters
Parameters tend to affect the behavior of the algorithms mainly the number of iterations, error tolerance, etc. Algorithms having a large number of parameters have to be put through most trial and error iterations to find the right combination. Even though more number of parameters guarantee flexibility, accuracy, and training time will take a hit while finding the right settings.
Number of Features
The number of features in certain datasets may be larger than the number of the data points for instance in the case of textual and genetics data. This may sometimes lead to a long training time that is not feasible.
RightClick.AI is known for creating high-performing models with good features that uncover actionable insights from data. We specialize in delivering top-notch Machine Learning services that act as a core to the success of digitally native businesses by enhancing customer experiences and creating new products on an enterprise level.
Reach out to us @ firstname.lastname@example.org to know how we can help build your Machine Learning solution that adds great value to your business.