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This article mainly introduces "what is the difference between machine learning and predictive analysis". In daily operation, I believe that many people have doubts about the difference between machine learning and predictive analysis. I have consulted all kinds of materials and sorted out simple and useful operation methods. I hope it will be helpful to answer the doubts about "what is the difference between machine learning and predictive analysis". Next, please follow the editor to study!

VS Predictive Analysis of basic Operation of Machine Learning

As mentioned above, machine learning is a science and technology in which computers accumulate knowledge independently to learn and imitate human behavior. The machine acquires data and information by observing and connecting with the real world, and processes the data stream in an auxiliary and non-auxiliary way.

Auxiliary machine learning will run pre-set patterns, invoke behaviors in the library and artificially input data, so that the machine can learn more accurately. On the other hand, non-auxiliary machine learning relies entirely on machines to recognize these patterns and then distinguish various behaviors in the data stream.

Predictive analysis is similar to auxiliary machine learning in many ways, which is why experts in the field of AI have always regarded predictive analysis as a branch of machine learning. In other words, not all predictive analysis and predictive analysis models can be classified as machine learning.

Because predictive analysis uses historical data for descriptive analysis. The process calculates and analyzes the additional data flow based on historical data and using the parameters set in the previous prediction analysis process. In most cases, the rules and patterns of the analysis will be consistent. Therefore, compared with machine learning, predictive analysis is static and has low adaptability.

Differences in pattern recognition

From the above description, it is not difficult to see that the main difference between machine learning and predictive analysis is that predictive analysis depends on the model set in advance, but it is difficult to adapt to the new data flow, while machine learning is more intelligent. it adjusts patterns and parameters according to the data flow encountered.

In addition, the models used by the two are different. Models such as data set processors and mainstream classifiers are used in predictive analysis; machine learning is more advanced, using Bayesian networks and deepening learning.

In addition, the update ways of the two models and parameters are not the same. For predictive analysis, any changes in analytical models or parameters need to be handled by data scientists. Without artificial input, there will be no random response of the analytical model to the data flow. But machine learning can update the model automatically.

It is also worth noting that the differences between the two are different. Predictive analysis focuses more on use cases. Because parameters and patterns are artificially entered into the analysis model, specific predictive analysis process use cases are determined by data scientists. Machine learning is completely data-driven, so changes in data flow will affect the analysis of it by AI.

Advantages and disadvantages

It's hard to say which is better than the other. Although machine learning technology is more advanced and flexible on the whole, accurate data must be guaranteed in order to create accurate statistical models. If the data are not up to standard, there will be biases in AI's recognition of any pattern or behavior.

Predictive analysis is more suitable for processing data streams because the specific parameters required, especially those analytical parameters, can be set by data scientists. In the process of predictive analysis, in order to ensure the accuracy of the analysis results, it is necessary to adjust to a large number of historical data. The analytical model provides an in-depth understanding of past patterns and trends as the basis for analysis.

On the other hand, almost all predictive analysis models can take effect immediately. Once the historical data and analysis parameters are ready, the analysis model can be adjusted according to the situation to process the new data flow. The only trouble is that the predictive analysis model cannot adapt in the data flow.

Machine learning has to go through a long process before the analysis steps are executed. After all, in the calculation, the requirement for AI is to be able to understand different data streams and accurately identify the patterns in them, so as to accurately process new data and get reliable results. This learning process is the biggest difference between the two.

As readers can see, the two methods are different in many ways, but some are highly similar. However, it is safe to say that predictive analysis can be regarded as a part of the machine learning process, but it does not mean that all predictive analysis can be classified as machine learning.

At this point, the study of "what is the difference between machine learning and predictive analysis" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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