What is the difference between machine learning and predictive analytics?

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The use of cognitive learning is more common today than ever before. Generally speaking, cognitive learning and cognitive computing are operational processes or technological platforms involving AI technology and signal processing.

AI is a new star in stimulating business, replacing the investment of capital and labor. It also has the potential to generate new forces that will drive business by changing the way people work and elevating their roles. And more and more fields are being won over by AI because it can process data, look for patterns in it, and learn to recognize behaviors with astonishing speed.

Machine learning, the basic learning process in any AI, represents the ability of machines to navigate data streams and recognize patterns and logical systems. This process can be done with either auxiliary or unauxiliary computing, although the latter is preferred in most cases.

In fact, the learning ability of a machine is very much the same as the predictive analysis ability, and when we talk about assisted machine learning (data flow with pre-determined data patterns), we are talking about another form of predictive analysis.

So what’s the difference? What are the similarities? Are two terms interchangeable?

Basic operations in machine learning VS predictive analytics

As mentioned above, machine learning is a science and technology in which computers accumulate knowledge autonomously to learn and imitate human behavior. Machines acquire data and information by observing and connecting with the real world, processing data streams in both auxiliary and unauxiliary ways.

Assisted machine learning runs pre-set patterns, calls actions in libraries and human input data so that the machine can learn with greater precision. Unaided machine learning, on the other hand, relies entirely on machines to recognize these patterns and then distinguish between behaviors in the data stream.

Predictive analytics is similar to assisted machine learning in many ways, which is why experts in AI have long viewed predictive analytics as a branch of machine learning. In other words, not all predictive analytics and predictive analytics models can be classified as machine learning.

Because predictive analytics uses historical data for descriptive analysis. The process calculates and analyzes additional data flows based on historical data, using parameters that have been set during the previous predictive analysis. For the most part, the rules and patterns are consistent. Therefore, compared with machine learning, predictive analysis is static and less adaptable.

Differences in pattern recognition

From the above description, it is easy to see that the main differences between machine learning and predictive analysis are as follows: Predictive analysis relies on pre-set models, but it is difficult to adapt to new data flows; Machine learning, on the other hand, is more intelligent, adjusting patterns and parameters based on the flow of data it encounters.

Moreover, the models used are different. Predictive analysis uses models such as data group processors and mainstream classifiers; Machine learning is more advanced, using Bayesian networks and deep learning.

In addition, the updating approaches of models and parameters are also different. For predictive analytics, any changes to the analysis model or parameters need to go through the data scientist. Without human input, there would be no random strain of the analysis model in the face of data flow. But machine learning can update the model automatically.

Another point worth noting is that the two points are different. Predictive analytics focuses more on use cases. Because parameters and patterns are manually entered into the analysis model, specific predictive analysis process use cases are determined by the data scientist. Machine learning is entirely data-driven, so changes in the flow of data affect how the AI analyzes it.

The advantages and disadvantages

It’s hard to say which is better. While machine learning is generally more advanced and flexible, accurate data is necessary to create accurate statistical models. If the data doesn’t fit the criteria, the AI will be biased in recognizing any pattern or behavior.

Predictive analytics are better suited for processing data streams because the specific parameters required, especially those for analysis, can be set by the data scientist. In the process of predictive analysis, in order to ensure the accuracy of analysis results, a large number of historical data need to be transferred. The analytical model provides insight into past patterns and trends as a basis for analysis.

On the other hand, almost all predictive analysis models work immediately. Once the historical data and analysis parameters are ready, the analysis model can be adjusted accordingly to handle the new data flow. The only trouble is that predictive analysis models do not adapt to the data stream.

Machine learning has to go through a longer process before the analysis steps can be performed. After all, in computing, AI needs to be able to understand different data streams and accurately identify patterns in them so that it can process new data accurately and produce reliable results. This learning process is the biggest difference.

As the reader can see, the two approaches differ in many ways, but are highly similar in some. It’s safe to say that predictive analytics can be part of the machine learning process, but not all predictive analytics can be classified as machine learning.