Robert Long
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Overfitting in Linear Regression
22 votes

In linear regression overfitting occurs when the model is "too complex". This usually happens when there are a large number of parameters compared to the number of observations. Such a model ...

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How do tech-companies employ Random Forest on large data sets?
4 votes

For the Random Forests algorithm, the time complexity for building a complete un-pruned tree is $O(m.n\log(n))$, where $n$ is the number of records/instances and $m$ is the number of variables. The ...

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is SST=SSE+SSR only in the context of linear regression?
Accepted answer
3 votes

if suppose regression wasn't linear, would SST=SSE+SSR still hold? If yes, why? If no, why? Just to be clear that with linear regression it is perfectly OK to model nonlinear associations such as $y =...

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Question about scaling in (R) Principal Components Regression
3 votes

I agree that if scaling is used on the training data, it should also be used on the test data. However, from what I see in the pcr documentation there is not scale option in the function. This seems ...

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What is the main difference between Hadoop and Spark?
3 votes

Hadoop is a framework for the distributed storage and processing of big data on the Hadoop File System (HDFS) where data is stored in a cluster of "nodes" and can be set up to be fault ...

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Imagining a Linear Regression model with more than 3 dimensions
3 votes

Humans live in a 3 dimensional world. Consequently it can be hard for us to visualise anything beyond 3 dimensions. For 4 dimensions, we can perhaps think about time as the next dimension, so we might ...

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How useful is Bayesian Inference
Accepted answer
2 votes

This may be an unpopular opinion to some, but in my experience Bayesian statistics is not particularly useful in data science in industry, for a couple of reasons: A Bayesian approach is very useful ...

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where does the database created in spark go?
2 votes

Where is this database created? A powerful paradigm in modern data storage and processing is the separation of compute and storage. Building systems with decoupled compute and storage has benefits ...

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Interpreting confidence interval results for datasets
1 votes

No, that isn't what it means. For one thing it is not clear what parameter the confidence interval that you calculated is for. In any case, some care is needed in the interpretation of (frequentist) ...

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How do you predict a continuous variable when all your independent variables are categorical
1 votes

A good place to start is with Analysis of Variance (ANOVA) models. The simplest case is where the response/outcome variable is continuous and you have 1 categorical predictor. This is called one-way ...

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How can I aggregate/combine 3 columns of a data frame into one column with the sum of the values of the other three in R?
0 votes

In base R you can do this quite easily with: # creates new variable as sum of the 3 existing ones data$GR_S01_w1 <- data$GR_S01_w1_a + data$GR_S01_w1_b + data$GR_S01_w1_c # remove the 3 existing ...

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Am I supposed to be using Mixed Effects?
0 votes

I always thought that the mixed effects model was for logistic regressions when my dependent variable was categorical This is not true at all. We use mixed effects, in particular random intercepts ...

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