22 Jul Difference Between Machine Learning And Statistic
If you’ve been wondering what machine learning and statistical learning is, then I will implore you to take your time to read and digest this blog post to help you in your new height. I will also discuss the difference between machine learning and statistics which you will definitely find useful when you take our courses in September.
Machine learning is all about predictions, supervised learning, and unsupervised learning, while statistics is about the sample, population, and hypotheses. But are they actually that different? Trust me they are different and many people have doubts about it, although they both have the same objective.
Nowadays, both machine learning and statistical techniques are used in pattern recognition, knowledge discovery and data mining. The two fields are converging more and more even though the below figure may show them as almost exclusive.
Machine learning is a subfield of computer science and artificial intelligence. It deals with building systems that can learn from data, instead of explicitly programmed instructions while a statistical model, on the other hand, is a subfield of mathematics. Machine learning is comparatively a new field.
Cheap computing power and the availability of large amounts of data allowed data scientists to train computers to learn by analyzing data. But, statistical modeling existed long before computers were invented. The difference between the two is that machine learning emphasizes optimization and performance over inference which is what statistics is concerned about.
Machine learning requires no prior assumptions about the underlying relationships between the variables. You just have to throw in all the data you have, and the algorithm processes the data and discovers patterns, using which you can make predictions on the new data set. Machine learning treats an algorithm like a black box, as long it works. It is generally applied to high dimensional data sets, the more data you have, the more accurate your prediction is.
In contrast, statisticians must understand how the data was collected, statistical properties of the estimator (p-value, unbiased estimators), the underlying distribution of the population they are studying and the kinds of properties you would expect if you did the experiment many times. You need to know precisely what you are doing and come up with parameters that will provide predictive power. Statistical modeling techniques are usually applied to low dimensional data sets.