Choosing the right Machine Learning algorithm is a tough task as it plays a major part in the success of your AI project. You have to choose over a range of factors before deciding on the one that best suits your use case or business problem. In this blog, we will take you through a list of major factors that helps you in selecting the right model for a particular task.
Before we start, let’s have a look at the different types of Machine Learning algorithms:
In supervised learning, the algorithm uses training data having both input and output labels to create a mathematical model.
In unsupervised learning, the algorithm uses data that only has input features without any output labels to build a model.
In reinforcement learning, the model performs a set of actions and makes decisions. It then improvises itself by learning from the feedback from its previous actions and decisions.
Important Factors Worth Considering While Choosing a ML Algorithm
The first and foremost factor you need to consider while choosing an algorithm is your data. You need to understand the data type, its characteristics, and size by visualizing the data and identifying the hidden patterns in it.
You can categorize your data into input and output data. If the input data is labeled, then it is best to use a supervised learning model, or if otherwise, an unsupervised learning model will fit in. The type of your output data can also help in determining the right ML model. For instance, the regression model works better for numeric output data while for a set of groups, the clustering model is the best.
The means by which your data is formed also plays a role. For linear data, you may require a linear model whereas, for complex data, an algorithm like random forest will work.
The performance of your algorithm depends on the size of your training datasets. Algorithm having high bias or low variance classifiers work better for shorter datasets whereas, for larger datasets, algorithms with low bias or high variance will work better.
The accuracy of a model can be defined as its ability to predict the right outcome from its observation that can be close enough to the actual response for a particular observation set. The accuracy of your model is determined by the type of problem you are trying to solve.
Models can be categorized as flexible and restrictive based on the range of shapes they produce of the mapping function. Restrictive models produce a small range of shapes while flexible models produce a wide range of shapes.
Restrictive models are preferred when inference is the goal and you would like to achieve interpretability. Flexible ones are preferred when high-accuracy is your goal. The interpretability of a model decreases as its flexibility increases.
Speed here generally refers to training time. If you want to achieve higher accuracy, then you may have to train your model using larger training data which again requires a longer time. Speed & accuracy are opposite to each other. If you are short on time, use a simpler algorithm and if accuracy is more important to you, a more complex algorithm will be useful for your AI project.
Number of parameters & features
Parameters determine the behavior of an algorithm. Error tolerance, number of iterations, options between variants are some of the parameters that will affect how your algorithm behaves. Most of the time, the number of parameters determine the time needed to train and process the data. As the number of parameters increases, the training and processing time also increases.
Based on the number of data points, the number of features of a dataset varies. A dataset with a large number of features may bog down a few algorithms. It is best to use an algorithm such as SVM that will work for apps having a large number of features.
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