How do you select the right machine learning algorithm to get the most relevant results? With the multitude of algorithms out there, it can be a daunting process. There is a huge repertoire of data as well. Selecting the best algorithm will depend on the quality of the data, the size and nature of this data and the outcome you are expecting from the algorithm. It is a method of trial and error, and even the most experienced data scientists have to experiment before finding the solution.
However, we have identified some steps to simplify the approach to resolving most machine learning problems.
Finding the Right Fit
Your data can be categorized by input or by output. If your data is in a structured format with clearly labeled data, then it can be categorized as a supervised learning problem. Supervised learning algorithms look at past data and make future predictions based on past outcomes. For example, if we want a machine learning algorithm to predict the future price of petrol, then the petrol price of past years, along with supportive date is patterns are studied. This could include city-wise petrol price, political events, geographical events, economic growth, GDP and other related data. Once a pattern is identified, the supervised learning algorithm will use this pattern to predict unlabeled data, in this case, it would be the future price of petrol.
- Supervised Learning
Supervised learning can further fall into 3 categories. When existing data is being used to assign the data into groups, then that is called Classification. Two-choice classifications like assigning images as tea or coffee are called binomial classifications and multiple options are called multi-class classification. Regression is when future predictions are based on past information, like in the case of petrol prices. And Anomaly Detection is identifying data patterns that are suspect or unusual. This is done by using existing data as a reference point. Anything different from this pattern will trigger an alarm.
- Unsupervised Learning
Data that is unlabeled falls under the unsupervised learning problem category. Also, there’s the reinforcement learning problem which is when you want to optimize an objective function through interactions with the environment.
In unsupervised learning, data points have no labels associated with them. There is no dependent variable. The data is organized using cluster analysis to find patterns or some relation between unlabeled information. This will help in bringing about some structure to the unorganized data and also to simplify it for better understanding.
- Reinforcement Learning
Reinforcement Learning is optimizing a situation by letting the algorithm learn the situation and enabling interaction with the environment. For example, a robot can learn how to play chess by finding patterns of existing data of a chess player’s moves to predict the next move it should take. The algorithm is rewarded when the correct moves are made, based on which the algorithm tries to further improve the results of the next move. Reinforcement learning is commonly used in Robotics and IoT applications.
Activate the Algorithms
Once you’ve categorized the problem, you’ll be able to identify a set of algorithms that can be implemented, using the tools available. Implement all of the algorithms using a machine learning pipeline and carefully identify the evaluation criteria. It will compare the performance of each of the algorithms and the best one will automatically get selected.