2021年1月27日 · Data-driven modeling: The process of using data to derive the functional form of a model or the parameters of an algorithm. Machine learning: The process of fitting parameters to data to minimize a cost function when the model is applied to the data.

2021年7月6日 · Then, once you get a value in this training data you have to use the obtained values on the testing data. And yes - simple things like centering the data by subtracting a feature-wise mean is also "learning". So you get the mean in the training step and subtract this training-data-obtained mean in the testing stage.

2015年6月21日 · Could I have an explanation on what are parameter driven models and what are observation driven models as categorized by Cox (1981) in Statistical analysis of time series: some recent developments ...

In contrast, in prediction it is more data-driven and you are more open-minded about relationships, because you are not searching for causality but rather for correlation.

2018年6月22日 · You ask about "exploratory data analysis", but you also include the [descriptive-statistics] tag & your final question is whether descriptive statistics is important. In this context do you only mean computing various descriptive statistics when you mention EDA, or are you asking about both descriptive statistics & EDA? I ask because many people (including me) think of …

2021年9月1日 · In my opinion it all depends, on the aim of modeling. If your main aim is to estimate properly design statistic model, then removing outliers that are not results of error, or differ significantly may results in estimation of coefficient which differs significantly from real ones.

2014年12月14日 · A statistical model can be seen as a procedure/story describing how some data came to be. A Bayesian model is a statistical model where you use probability to represent all uncertainty within the model, both the uncertainty regarding the output but also the uncertainty regarding the input (aka parameters) to the model.

Actually, I thought I had understood what one can show a with partial dependence plot, but using a very simple hypothetical example, I got rather puzzled. In the following chunk of code I generate ...

Jerome Friedman wrote a paper a while back: Data Mining and Statistics: What's the Connection?, which I think you'll find interesting. Data mining was a largely commercial concern and driven by business needs (coupled with the "need" for vendors to sell software and hardware systems to businesses).

2021年11月15日 · It is wrong that, if the sample is big enough the distribution of the data/population approaches a normal distribution. Instead, the CLT relates to (the limit of) the mean of samples (or other types of sums of variables). But you are right that the sample distribution of the test statistic, which is used to estimate parameters of the population …