Which is better machine learning or classical statistics?
There is work to do and machine learning methods and deep learning methods hold the promise of better learning time series data than classical statistical methods, and even doing so directly on the raw observations via automatic feature learning.
How are machine learning algorithms compared to classical algorithms?
An important recent study evaluated and compared the performance of many classical and modern machine learning and deep learning methods on a large and diverse set of more than 1,000 univariate time series forecasting problems.
What is the difference between machine learning and predictive modeling?
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.
Why does machine learning need minimal human effort?
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. Because machine does this work on comprehensive data and is independent of all the assumption, predictive power is generally very strong for these models.
What is the curse of small datasets in machine learning?
This is Part 1 of Breaking the curse of small datasets in Machine Learning. In this part, I will discuss how the size of the data set impacts traditional Machine Learning algorithms and few ways to mitigate these issues.
Where can I learn more about classical statistics?
In the very first part of Brian’s video, he mentioned his executive data science lectures where you can learn more about machine learning vs. classical statistics. Here is the first video in that series discussing the topic.