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If you are a beginner and just started machine learning or even an intermediate level programmer, you might have been stuck on how do you solve this problem. Where do you start? and where do you go from here?
In Machine Learning, there’s no single solution that can fit all and multiple solutions to a problem can exist. With lots of varieties of algorithms, choosing the right algorithm for your problem can become a daunting task.
Don’t worry! in this article, we will be simplifying your approach in Machine Learning with a cheat sheet that you can use to select the right algorithm suited for your problem.
There are several factors that can affect the decision of choosing the right algorithm. Some problems are specific and require unique approaches. For Example, a recommendation system is used to solve a very specific kind of problem. While some type of problems are open and they require trial and error method. Supervised Learning, classification, and regression are open types of problems.
To use the cheat sheet you just have to look at the labels on the chart as decisions and move towards the arrow that answers the question. For example,
It is worth mentioning that even an experienced data scientist can’t tell which algorithm will perform best without trying different algorithms. This cheat sheet may not be the only solution for your problems and there may be multiple paths for the same task. This cheat sheet is only hoped to provide you with some guidance on what algorithms can be used based on the known factors.
Supervised learning algorithms involves direct supervision of operation. We teach or train the machine using data, which means that the data is labelled with the right answer. We use an algorithm to analyse the training data and learn the function that maps inputs with their outputs. The function can then be used to predict output of unknown inputs by generalising from training data. Supervised learning is basically used for two types of problems.
Supervised learning requires labelled data, which can be challenging to find or generate if someone else didn’t work on a similar project. In a semi-supervised approach, we use some of the labelled data with unlabelled data.
As you can see, the data is not fully labelled that is why this is called semi-supervised learning. The model’s accuracy is improved by using labelled data with unlabelled data.
Unsupervised learning is used for unlabelled data. The machine has to discover patterns, similarities and differences that lies in the data without any supervision. The perform clustering and reducing the number of dimensions.
Reinforcement learning optimizes the behaviour of an agent, based on the feedback from the environment. An agent rewards the machine when it makes the right decisions and penalties for its bad decisions. This learning doesn’t require us to collect data and then clean data. It is a self-sustained system that tries to improve itself in the real world. AlphaGO, a computer program based on reinforcement learning beats the best Go player in the world.
Machine Learning problems can be solved in numerous ways, and you can choose algorithms based on several factors like accuracy, objective, size, and nature of data. You can refer the cheat sheet and get a head start in building the model. Once, you have implemented the solution and got results then you can further explore different algorithms to see what is the best algorithm that is suited for that particular problem.