How to choose the right Machine Learning Algorithm?
Machine learning shares both aspects of art and science. When considering machine learning algorithms, you will find there is no particular solution or one approach that fits all. There are numerous factors that can affect your decision to choose an ML algorithm.
Some problems are very explicit and require a unique approach. For instance, if you look at a recommendation system, it’s a very common type of machine learning algorithm and solves a very exact kind of problem. While some other problems are open and need a trial and error approach such as supervised learning, classification and regression. They could be used in anomaly detection or could be incorporated to build more universal sets of predictive models.
Further, some of the decisions that we make when choosing an ML algorithm have less to do with the optimization of the algorithm but more to do with business decisions. Here we compiled some of the factors that can help you narrow down the search for your machine learning algorithm.
1. Understanding the Data
The type and kind of data play a vital role in determining which algorithm to practice. Some algorithms can work with smaller sample sets while others require tons of samples. Few algorithms work with certain types of data sets e.g. Naïve Bayes works well with definite input but doesn’t respond to missing data.
2. Recognize your constraints
• Check data storage capacity in order to store gigabytes of classification or gigabytes of data to the cluster.
• In real-time applications, it is obviously very important to have a swift prediction
• Data learning have to be fast in order to rapidly update your model with a different dataset.
3. Identify the available algorithms
Once you understand where you stand, you can identify the algorithms that are applicable and tangible to implement. Some of the elements persuading the choice of a model are:
• Whether the model meets the goal of the business
• The accuracy of the model
• How reasonable the model is
• Performance and time it can take to build a model to make the right predictions.
• Scalability of the model
4. Logistic Regression
Logistic regression provides a probabilistic framework to receive more training data in the future that you want to be able to quickly incorporate into your model. Logistic regression can also help you comprehend the contributing factors behind the prediction.
5. Decision trees
Decision trees can easily handle feature interactions and they’re non-parametric, so you don’t have to worry about outliers. One drawback is that they don’t support online learning, so you have to rebuild your tree when new examples come up.
6. Support Vector Machine
Support Vector Machine is a supervised ML technique that is broadly used in pattern recognition and classification problems.
7. Naive Bayes
Naive Bayes is known to outperform even highly sophisticated classification methods and used for very large data sets.
8. Neural networks
It is used to predict the class by establishing a link between neurons. With Neural networks, extremely complex models can be trained and utilized to perform unsupervised learning tasks, such as feature extraction from raw images or speech with much less human intervention.
It is difficult to shortlist at first which algorithm will work best. Being able to combine and balance to solve a machine-driven problem is crucial and those who can do this add the most value. So consider all the points above to develop the right solution and at the end assess the performance of the algorithms to select the best one.