Today, the popularity of machine learning is on the rise. More and more organizations use this technology to predict customer demand, drive inventory forecasting, and optimize operations. According to a recent research study, AI received an investment of more than $8 billion in 2016. Let’s take a look at 7 tips that can help organizations get the most out of machine learning.
1. Review the data
It takes time to prepare a training data set. During this process, errors may occur from time to time. Therefore, before you start working on a model, we suggest that you do a data review. This will help you know if the required data is error free.
2. Cut the given data
Usually there are different structures in the data. So you may want to slice your data like you would slice a pizza. Your goal is to build separate models for the slices. After you have identified a goal, you can build a decision tree. You can then build different models for the segments.
3. Make use of simple models
It is important to build complex models in order to extract information from the data. Simple models are much easier to implement. Plus, they make the explanation process much easier for key business stakeholders.
What you need to do is build simple models with decision trees and regression. Also, you should use an assembly model or gradient boost to ensure the functionality of your models.
4. Identify rare events
Machine learning often requires unbalanced data. Therefore, you may find it difficult to correctly classify rare events. If you want to counteract this, we suggest creating biased training data through undersampling or oversampling.
This will help balance the training data. Apart from this, the higher event ratio can help the algorithm to differentiate between the event signals. Decision processing is another strategy to put much more weight on event classification.
5. Combine several models
Typically, data scientists use different algorithms such as random forests and gradient boosting to build many models. Although these models generalize well, you can choose the ones that fit best given certain data limits. An easy way to overcome this problem is to combine various modeling algorithms.
6. Implement the models
It often takes a few weeks or months to deploy models. Some models are not implemented at all. For best results, you may want to determine business goals for managing the data and then monitoring the models. Apart from this, you can use tools to capture and link data.
7. Autotune models
You must assign algorithm options known as hyperparameters before creating a machine learning model. In fact, the automatic adjustment helps to identify the appropriate hyperactive parameters in a short period of time. And this is one of the biggest benefits of autotuning.
In short, here are the 7 tips that can help you develop effective machine learning models. Hopefully, you’ll find these tips very helpful throughout your projects.