For any given machine learning problem, many algorithms can be applied and several models can be generated. A spam detection classification problem, for example, can be solved using a variety of models, including naive Bayes, logistic regression, and deep learning techniques like LSTMs. In this article, I will tell you how to choose a machine learning algorithm for a particular task out of so many algorithms.
Having a plethora of options is good, but deciding which model to implement in production is crucial. Although we have multiple performance metrics to evaluate a model, it doesn’t make sense to implement every algorithm for every problem. It takes a lot of time and work. It is therefore important to know how to choose the right machine learning algorithm for a particular task.
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In this article, we’ll take a look at the factors that can help you choose a machine learning algorithm that’s best for your project and your particular business needs. To do this, we’ll look at various factors that can help you narrow down your selection. By understanding all these factors below will help you in understanding the task that your machine learning model will perform.
Learn How to Choose an Algorithm
When we talk about the interpretability of an algorithm, we are talking about its ability to explain its predictions. An algorithm that is lacking in such an explanation is called a black-box algorithm.
Algorithms like k-nearest neighbor (KNN) have high interpretability due to the importance of features. And algorithms like linear models have an interpretability thanks to the weights given to the characteristics. Knowing how interpretable an algorithm becomes important when you think about what your machine learning model will ultimately do.
For classification issues such as detecting cancer cells or assessing credit risk for home loans, one must understand the reason for the system’s results. Getting a prediction is not enough, because we need to be able to assess it. And even if the prediction is correct, we must understand the processes that lead to those predictions. If understanding the reason for your results is a requirement of your problem, an appropriate algorithm should be chosen accordingly.
Number of Data Points and Features
When you try to choose an appropriate machine learning algorithm, the number of data points and features play a critical role. Depending on the use case, machine learning models will work with a variety of different data sets, and those data sets will vary in terms of data points and functionality.
In some cases, selecting a model comes down to understanding how the model handles datasets of different sizes. Algorithms like neural networks work well with big data and a large number of features. But there are some algorithms like Support Vector Machine (SVM) that work well with a limited number of features. When selecting an algorithm, be sure to consider the size of the data and the number of features.
Data often comes from a mix of open source and custom data resources, and therefore can also come in a variety of different formats. The most common data formats are categorical and numeric. Any given data set can contain only categorical data, only numeric data, or a combination of both.
Algorithms can only work with numeric data, so if your data is categorical or non-numeric format then you will need to consider a process to convert it to numeric data.
Training time is the time it takes for an algorithm to learn and create a model. For use cases like recommending movies to a particular user, the data should be trained every time the user logs in. But for use cases like inventory prediction, the model needs to be trained every second. It is therefore essential to consider the time required to train the model.
Neural networks are known for the considerable time it takes to train a model. Some Traditional machine learning algorithms like K-Nearest Neighbors and Logistic Regression take very less time. Whereas algorithms, like Random Forest, require different training times depending on the processor cores used. So this is also an important criterion when you choose a machine learning algorithm.
If your entire dataset can be loaded into the RAM of your server or computer, there are a lot of algorithms you can apply. What is this is not possible, then you may need to adopt incremental learning algorithms.
Incremental learning is a method of machine learning in which the input data is continually used to extend the knowledge of the existing model, i.e. to train the model further. Incremental learning algorithms aim to adapt to new data without forgetting existing knowledge, so you don’t need to retrain the model.
So these are some factors that you can use to choose a machine learning algorithm from so many algorithms. I hope you liked this article on how to choose a machine learning algorithm. Feel free to ask your valuable questions in the comments section below.