Machine Learning vs Statistics
The question, “What's the difference between Machine Learning and Statistics?” has been asked now for decades. Machine Learning is largely. The machine learning practitioner has a tradition of algorithms and a pragmatic focus on results and model skill above other concerns such as. Aatash Shah, CEO of Edvancer Eduventures. ML vs. stats. Many people have this doubt, what's the difference between statistics and machine learning? Is there.
Linear relation between independent and dependent variable Homoscedasticity Mean of error at zero for every dependent value Independence of observations Error should be normally distributed for each value of dependent variable Similarly Logistic regressions comes with its own set of assumptions. Even a non linear model has to comply to a continuous segregation boundary. Machine Learning algorithms do assume a few of these things but in general are spared from most of these assumptions.
Also, we need not specify the distribution of dependent or independent variable in a machine learning algorithm. Types of data they deal with Machine Learning algorithms are wide range tools. Online Learning tools predict data on the fly. These tools are capable of learning from trillions of observations one by one.Machine Learning vs Statistics
They make prediction and learn simultaneously. Other algorithms like Random Forest and Gradient Boosting are also exceptionally fast with big data.
Machine learning does really well with wide high number of attributes and deep high number of observations. However statistical modeling are generally applied for smaller data with less attributes or they end up over fitting. Here are a names which refer to almost the same things: Formulation Even when the end goal for both machine learning and statistical modeling is same, the formulation of two are significantly different.
What’s the difference between machine learning, statistics, and data mining?
Predictive Power and Human Effort Nature does not assume anything before forcing an event to occur. So the lesser assumptions in a predictive model, higher will be the predictive power. Machine Learning as the name suggest needs minimal human effort. Machine learning works on iterations where computer tries to find out patterns hidden in data.
No, Machine Learning is not just glorified Statistics
Because machine does this work on comprehensive data and is independent of all the assumption, predictive power is generally very strong for these models. Statistical model are mathematics intensive and based on coefficient estimation. It requires the modeler to understand the relation between variable before putting it in. End Notes However, it may seem that machine learning and statistical modeling are two different branches of predictive modeling, they are almost the same.
The difference between these two have gone down significantly over past decade. Both the branches have learned from each other a lot and will further come closer in future. I hope we motivated you enough to acquire skills in each of these two domains and then compare how do they compliment each other. Machine learning treats an algorithm like a black box, as long it works. In contrast, statisticians must understand how the data was collected, statistical properties of the estimator p-value, unbiased estimatorsthe 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 the predictive power. Statistical modeling techniques are usually applied to low dimensional data sets.
Conclusion It may seem like machine learning and statistical modeling are two different branches of predictive modeling. The difference between the two has reduced significantly over the past decade. Both the branches have learned from each other a lot and will continue to come closer together in the future.
But, understanding the association and knowing their differences enables machine learners and statisticians to expand their knowledge and even apply methods outside their domain of expertise.
Collaboration and communication between these two fascinating data-driven disciplines allows us to make better decisions that will ultimately positively affect the way we live. He has over 6 years of experience in the investment banking industry before starting Edvancer.