Big Data has been one of the latest industry buzzwords. It is driving a revolution in many business sectors, especially in the industrial sector, and it won’t stop anytime soon. With so much data being churned out every day, there is a race to find the best way to harness this data and extract as much actionable insights and information as possible.
The first step of any analysis is to determine which data sources are the most meaningful. Nowadays, it's pretty standard for large companies to collect mountains of machine-generated data that can be analyzed with machine learning algorithms that allow for the generation of predictive maintenance scheduling or to test current production patterns within their systems. This becomes more important when dealing with industrial machinery, where failure and downtime can have catastrophic consequences.
When empowered by machine learning, businesses operating with industrial processes – like manufacturing plants or power stations – to better anticipate when parts will fail, maintaining more efficient production schedules. Knowing how to make these predictions can also open up new business opportunities that allow companies to invest in future business deals predicated on defined data driven performance expectations.
The most common form of machine learning is supervised learning. You give the machine (or algorithm) a set of input data and corresponding output data, which usually done by labeling each piece of input data with its proper output. Then this system learns from the different parameters within the given dataset so that when presented with unseen information, it can attempt to predict an appropriate actionable response. This is great for many uses, but there are some significant drawbacks.
One of these drawbacks is overfitting. This occurs when a machine learning algorithm works perfectly with the given dataset but fails to work when presented with an entirely new data. This occurs when a system has memorized a dataset and is consequently is not learning any generalizable rules. This means even though it may be accurate in predicting future patterns within its training data, it's generally not precise at finding broader trends within large bodies of unstructured information.
Another drawback is how much data you need to use machine learning for analysis. Machine learning usually requires large amounts of labeling information so that the system can learn from past mistakes and improve itself through experience.
However, having lots of input data does not necessarily mean that your model will perform better than another model that uses fewer input data points, especially if there are fewer patterns present in the data.
This means that you need to find an acceptable balance between having too little and too many data points. If you have a minimal amount of input data, then it may be difficult for your system to learn from past mistakes within smaller datasets. However, suppose you have a large quantity of input data.
In that case, there isn't sufficient room for it to build up any learning or experiences with its dataset resulting in similar overfitting problems. Large amounts of information are not necessarily valuable and won’t guarantee that the machine will learn any better than before. Finding the proper quality and amount data points remains necessary to benefit from machine learning.
Another consideration is what type of machine learning algorithm you use. Some algorithms are fundamentally better than others at specific tasks, so knowing which model to use can be difficult.
Suppose you've got a large dataset with lots of different possible outcomes. In that case, it's probably best to use a model that can identify subtle patterns within your data. This is known as a nonparametric algorithm such as k-Nearest Neighbors or Quadratic Discriminant Analysis (although these algorithms may not perform as well on datasets with more than two possible outcomes).
These types of algorithms are also suitable for detecting outliers within the dataset, meaning that they're very good at detecting abnormal behavior within your data set and flagging it for review.
On the other hand, if you've got limited input data and want to predict the exact output value for each piece of input data, then you should use a parametric algorithm such as Linear Regression.
These algorithms can look at the input data and come up with an equation for predicting the output value based on the different parameters within this dataset (regardless of whether there are any underlying patterns within your data or not).
3. Combined Metric Methods
It's often best to combine both nonparametric and parametric algorithms so that you can get the benefits from each type of model. This typically results in better performance than either one alone but also requires more time and effort in determining how to effectively blend these two algorithmic models.
If you're short on time, then it may be more beneficial to implement one or the other rather than sprinting to find some middle ground between these two.
When it comes to choosing which machine learning algorithm you should use, there are a variety of methods that you can use to help determine which model will work best with your data, such as cross-validation, bootstrapping, and the holdout method.
However, these all require at least some of your input data, and many won't even give you accurate results if you don't have enough input data for them to draw conclusions. This makes finding an optimal configuration for machine learning problematic when working with big datasets. Often, it's difficult to accurately measure the quality of a model without more comprehensive information than a single dataset, no matter how large.
Furthermore, it is essential to note that you can't use your test data as your training data for the machine learning model because this will mean that it's unable to compare past mistakes and learn from them. This means that you need another dataset (preferably separate from the one above) that provides a benchmark for measuring your performance (although there are ways of achieving this without creating an entirely new dataset).
Finally, suppose you're using big datasets. In that case, overfitting may become more likely because there is too little input data for your system to classify all of the different scenarios within these datasets correctly. Overfitting occurs when the machine has memorized all of the input data without actually knowing how each piece of information correlates.
Author bio: Lucy Jones is a Business Advisor at Remote DBA. She shares her tips on business & marketing.
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