With machine learning, often, people can’t keep up with all the new models out there, so they tend to rely on the ones they trust the most. But the chances are that they can fall into sub-optimal results. Today, we’re going to narrow down the options quickly and efficiently. Yes, how to choose the best machine learning model? Scroll down for more.
How to choose the best machine learning model?
Contrary to popular belief, the best-performing machine learning model doesn’t always indicate the best solution. On the competition front, performance matters most. Real-world situations will always take other factors into account.
To begin with, let’s talk about the model’s performance and consider other aspects when choosing a model.
Choosing a model should be based on the quality of its results. Make sure that you focus on algorithms that offer the best performance. Different metrics could help analyze the model results, depending on the problem at hand. For example, accuracy, precision, recall, and f1-score are all popular metrics.
Not all metrics are applicable in all situations. Accuracy, for instance, does not apply to imbalanced datasets. Before selecting a model, selecting a metric (or set of metrics) to measure its performance is an important step.
Ease of explanation
Often, it is essential to explain a model’s results. Due to the complexity of many algorithms, the results can be hard to understand regardless of how good the algorithm is.
In these situations, the lack of explanation is often a deal-breaker. The ease of interpretation of each model is crucial before selecting a model. It’s interesting to note that explainability and complexity are usually on opposite ends of the scope, so let’s move on to complexity.
Amount of complexity
It’s fun to figure out more interesting patterns in data with a complex model, but it’s harder to explain and maintain.
Complexity can improve performance but increase costs as well. Complex setups have an ever-increasing impact throughout the model’s lifetime.
Size of the dataset
If you choose a model, one of the most important factors is how much training data is available. Additionally, you should consider how much information you really need to achieve good results and the amount of data available. A great solution can sometimes be built with 100 training examples; other times, it takes 50,000. Select a model that can handle your problem and the amount of data based on this information.
A dimension-based perspective
Using dimensionality in two different ways is helpful: the size of the vertical axis represents the number of data points in the dataset. Horizontal dimensions represent the features within the dataset. As we discussed previously, the vertical dimension influences model selection. A model with more features will often produce better solutions by considering the horizontal dimension. The complexity of the model increases as well. With high-dimensional datasets, not every model scales similarly. If high-dimensional datasets become a problem, sophisticated dimensionality reduction algorithms may be required.
Costs and time involved in training
What is the training time and cost for a model? Would you choose a model with 98% accuracy that costs $60,000 to train or one with 97% accuracy that costs $6,000?
Depending on your situation, the answer will be different.
The key to designing a scalable solution is to balance time, costs, and performance. A model that incorporates new knowledge in near real-time cannot withstand long training periods. The training cycle of a recommendation system, for instance, requires constant updating based on each user’s actions.
The inference time
Do models and predictions take a long time?
Suppose there is a self-driving vehicle: its decisions need to be made in real-time, so models that require too much time to run are unsuitable. Predictions using KNN require a substantial amount of processing during inference time, for example. Because of this, running them is expensive. A Decision Tree will, however, need more time during training and be lighter during inference.
A great deal of attention is paid by most people to their favorite model. A lot of the time, this is the one they know best and is the one they have had good results with previously. However, in the world of machine learning, there is no such thing as a free sample. When we consider the limitations of real-life systems, it is apparent that there is no single model that will work in every situation.