sin7

There's a free community version.

I misread your comment. Sorry about that. I misread it because I think of a generation as twenty years and a lifetime as what you described. Sorry again.

De facto is not the same as official.

Lies, damn lies and then this. 85% counts Hispanic people as white. The 35% does not. Ain't no way this is an innocent mistake. Hispanic people have always been a large portion of California.

If a 'fair' coin came up 70% you lose, you'd be ready to fight the person trying to scam you. At the very least ready to call the cops on them.

Yeah. So?

Make no mistake about it, there is no argument. We are not negotiating. I'm not recognizing anything that was said. Like hearing my nephew cracking up over saying poop, it was cute the first time. It's time for new material yungin'.

Even worse, a good portion of the population believes that a good portion now believes that ... whatever you just said. And this good portion is annoying. Bring up nonsense like this in conversations where it does not fit, in the middle of dinner, in the middle of pumping gas. Like damn, I'm watching out for one of those kids with a gun just waiting to exhibit his mental health and you want me to worry about the guy in a dress?

Sir, you have made my day. This is the funniest thing related to Joe Rogan.

Thank you.

Luka is currently playing in his 5th year. He has 4 years behind him. Not the best comparison, but seems fair.

Can poor people take out a loan and create new money?

No need. Just attribute an unmeasurable quality such as creativity to your racial group.

Whites also have lower average scores on them. Lower than Asians and Jews.

At the elite level this would make a huge difference. At the state level not at all.

I already have a machine that makes white castle burgers at home. It's called a microwave. The results are not the best.

    runs <- 100000
    x <- vector(mode = "numeric", length = runs)
    for (i in 1:runs){
      while (sum(sample(1:8, size = 3, replace = TRUE)) != 24){
        x[i] <- x[i] + 1
      }
    }

    summary(x)
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
      0.0   146.0   353.0   511.8   708.0  5112.0 

    quantile(x, c(0.5, 0.8, 0.9))
     50%  80%  90% 
     353  824 1187

Strangely enough the mean agrees. The other ntiles are off a bit, but that's randomness for you.

    runs <- 10000
    x <- vector(mode = "numeric", length = runs)
    for (i in 1:runs){
      while (sum(sample(1:6, size = 3, replace = TRUE)) != 18){
        x[i] <- x[i] + 1
      }
    }

    summary(x)
    quantile(x, c(0.5, 0.8, 0.9)) 

    > summary(x)
       Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
        0.0    62.0   149.0   216.2   300.0  1902.0
    > quantile(x, c(0.5, 0.8, 0.9))
    50% 80% 90%
    149 350 495

A simple simulation. Run 10K times. Count the number of times it takes for three dice to add up 18.

The numbers very much agree with you. The median is 149. The 90th is 495 in the simulation, which is close enough to 496. There is very much a long tail in the data. So, the median and the average will not be the same. Is it a coincidence that mean is a 216?

    runs <- 10000
    x <- vector(mode = "numeric", length = runs)
    for (i in 1:runs){
      while (sum(sample(1:6, size = 3, replace = TRUE)) != 18){
        x[i] <- x[i] + 1
      }
    }

    summary(x)
    quantile(x, c(0.5, 0.8, 0.9)) 

    > summary(x)
       Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
        0.0    62.0   149.0   216.2   300.0  1902.0
    > quantile(x, c(0.5, 0.8, 0.9))
    50% 80% 90%
    149 350 495

A simple simulation. Run 10K times. Count the number of times it takes for three dice to add up 18.

The numbers very much agree with you. The median is 149. The 90th is 495 in the simulation, which is close enough to 496. There is very much a long tail in the data. So, the median and the average will not be the same. Is it a coincidence that mean is a 216?

What am I missing? 6 x 6 X 6 = 216 or about 7 months.

1*0 = 1 1*1 = 1 1*-1 = 1

2*0 = 1 2*1 = 2 2*-1 = 1/2

When 1 raised to any power equals 1, does the power matter at all? Even if it's unknown, the answer is 1.

Is it too deeply entrenched to change? The number of times I have had a data.frame grouped when it wasn't supposed to be, I can count on my fingers. But the hours that I spent trying to figure it out must amount to a paycheck or two.

Are you talking about black kids? Because I can assure you that no one gives a single fuck about their feelings or any uncomfortable differences.

PostgreSQL is not in the game. Completely different purposes. PostgreSQL is great for what it's meant to do. Analytics is not it. Reads are too slow.

The answer is yes. I used ClickHouse to calculate and forecast sales of products at a dozen or so stores. The compression was huge because it's essentially the same data every day except for changes to the inventory. At the time I checked vanilla PostgreSQL, TimeScaleDB and ClickHouse. It wasn't even close when it came to storage or performance. ClickHouse allowed me to work off of an old workstation where I installed Ubuntu.

In my case the data arrived in CSVs with around 20k skus. Had they arrived a couple at a time, I could have created a CSV and written to ClickHouse later or used any of the other storage methods available in ClickHouse.

FightCade2 is the most popular. Takes some work to set up, but has a lot of old games. No NES. Plenty of arcade though. There was Kaillera back in the day. I don't know where is stands now.

Not really. It's a size. You can get extra large, large, medium, small and personal. It's something like 16 in diameter for an extra large, 14 in for large, etc. The personal pizza is around 6 in.

I've been doing the data thing for a while. During one of my defenses of R, someone brought up that R was a black hole. That if you programmed in R, you were a user who just filled in the correct function arguments and it just spit out the answer. And that was when my thoughts on machine learning changed.

The vast majority of us are users. We massage the data to be in a certain shape, then feed it through a machine that someone else created. We can change the parameters. We can change the data. But few of us are going to look in to the code of a random forest function.

I've switched tracks and started doing web development. Playing with the hyper parameters in machine learning is no different than changing the feel of a drop down by changing the colors, fonts and other things to fit a certain aesthetic.

I could be wrong, but I have yet to meet anyone that has done anything besides use packages created by others to call themselves data scientists. I think that opens it up to becoming just another tool no different than Excel.

The headaches are pretty bad.

Some Russians too. Maybe all Russians.

Truth be told, I don't know and it matters very little to me. Yet I have read more than one article about Kenyans and hiding in the mountains from testers. Maybe it was propaganda, but I read it.

I have no dog in this fight. I'm sure Kenyans are great runners. Maybe the best.

I thought it was documented that Kenyans and Ethiopians benefited from steroids and EPO. It was all over the news the past decade or so.

Good data scientists also need to be great at a couple of things. They must be excellent presenters, and excellent diplomats. Getting the data is the hardest part. Data silos are create all over the place and getting access to that data requires speaking to a lot of higher ups.

The map is not the territory.

All models are wrong, some are useful.

Blah, blah, blah.