Is the difference between statistical models and machine learning models meaningful? The answer depends on the use case. Many current users of ML algorithms falsely believe that they can make accurate predictions from complex datasets with a limited number of observations.
While statisticians are quite good at recognizing the limitations of effective sample sizes, they usually stop short of incorporating models with higher levels of complexity than the information content of the sample. Nevertheless, a few key differences exist between statistical models and machine learning models.
Statistical models are often preferred by credit card companies, which use data to understand how to use credit card information. Statistical models are not black-box algorithms; credit card companies need interpretability and accuracy. Likewise, Netflix and Amazon need to identify relevant content for their users. They can get the job done with machine learning models and do so more efficiently. But the distinction between statistical and machine learning models is important for companies in various industries.
Statistical and machine learning models are closely related and sometimes overlap, but they have different purposes. Statistical models can be used to predict future events, but machine learning solutions can be simple probabilities or complex mathematical calculations. And when a decision-making problem requires the use of these models, machine learning solutions are often more efficient than traditional statistical models. So, which one should you use? A successful company will know when and how to apply them.
Statistical models use formal methods to model relationships between variables and predict future events. Machine learning allows algorithms to reason over new data and make predictions. One example is facial recognition software, which recognizes human emotions. Another application of machine learning is the development of supply chain planning software. As a result, it is possible to automate repetitive evaluations and decisions. This can lead to significant savings and benefits for organizations. The key difference between statistical and machine learning models is the quality of the data.
Statistical forecasting focuses on prediction. General-purpose learning algorithms search for patterns in data and identify the best course of action. In contrast, machine learning requires no explicit understanding of mechanisms. Statistical forecasting requires assumptions regarding the distribution of data and can be applied to different types of data. That means that some types of machine learning are able to predict a wide variety of data. So, in summary, statistical models are better at predicting future events.
In machine learning, computers analyze and classify data based on patterns and attributes. These models then train themselves to carry out tasks without manual programming. The training process is based on a large dataset with known values of explanatory variables. Using this data, the algorithms then predict the value of the target variable based on those values. And the more data they have, the better they perform. So, the answer to the question, “Are Machine Learning models statistical models?” Is yes.