How many times you have heard that your residuals should be independent, normally distributed with mean zero, and with constant variance?

No matter if your answer is zero or a million times. This thread can help you

It doesn't even matter that you don't know about residuals👇

First of all: What is a residual?

It is a synonym for error. It is the difference between the expected output and the output of our model:

Y - Ym

Where Ym is the output of our model. So residuals can be positive or negative and we need them to stay close to zero
1: Independence

We don't want the error in for some input to be dependent on the error for another input

That would mean there is information about the relationship between inputs and outputs that our model is missing and that is present in the residuals2
2: Mean equals zero

Well, this is an intuitive one, isn't it?

We have said already that we need residuals close to zero. There should be both negative and positive residuals.

But we don't want a single huge positive error and then a lot of small negative ones

Let's continue
3: Normally distributed

The mean of the residuals is zero, but that's not enough

We need about 50% of the errors to be negative and the other half to be positive

Also, we need most of the errors to be close to zero

In other words, we need the errors to be normally distributed
4: Constant variance

Last but not least. We need the errors to show a constant variance. This is a property called homoscedasticity 🥵

That means our model is equally good (or bad) for all inputs

Non-constant variance denotes our model is missing relevant information
Generally, if the residuals of your model don't meet all the above requirements we should dump it

But it might depend on the problem

Note that and include a residual analysis in your machine learning pipeline starting right now!!!
And that's it!

I hope you have enjoyed this thread and you have learned something new

I have been kind of inactive these days but I publish content like this one from time to time

So, if you like ML, AI, algorithms, and Comp-Sci in general, consider following

Stay tuned!

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The best morning routine?

Starts the night before.

9 evening habits that make all the difference:

1. Write down tomorrow's 3:3:3 plan

• 3 hours on your most important project
• 3 shorter tasks
• 3 maintenance activities

Defining a "productive day" is crucial.

Or else you'll never be at peace (even with excellent output).

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2. End the workday with a shutdown ritual

Create a short shutdown ritual (hat-tip to Cal Newport). Close your laptop, plug in the charger, spend 2 minutes tidying your desk. Then say, "shutdown."

Separating your life and work is key.

3. Journal 1 beautiful life moment

Delicious tacos, presentation you crushed, a moment of inner peace. Write it down.

Gratitude programs a mindset of abundance.

4. Lay out clothes

Get exercise clothes ready for tomorrow. Upon waking up, jump rope for 2 mins. It will activate your mind + body.

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"I lied about my basic beliefs in order to keep a prestigious job. Now that it will be zero-cost to me, I have a few things to say."


We know that elite institutions like the one Flier was in (partial) charge of rely on irrelevant status markers like private school education, whiteness, legacy, and ability to charm an old white guy at an interview.

Harvard's discriminatory policies are becoming increasingly well known, across the political spectrum (see, e.g., the recent lawsuit on discrimination against East Asian applications.)

It's refreshing to hear a senior administrator admits to personally opposing policies that attempt to remedy these basic flaws. These are flaws that harm his institution's ability to do cutting-edge research and to serve the public.

Harvard is being eclipsed by institutions that have different ideas about how to run a 21st Century institution. Stanford, for one; the UC system; the "public Ivys".
Trump is gonna let the Mueller investigation end all on it's own. It's obvious. All the hysteria of the past 2 weeks about his supposed impending firing of Mueller was a distraction. He was never going to fire Mueller and he's not going to


Mueller's officially end his investigation all on his own and he's gonna say he found no evidence of Trump campaign/Russian collusion during the 2016 election.

Democrats & DNC Media are going to LITERALLY have nothing coherent to say in response to that.

Mueller's team was 100% partisan.

That's why it's brilliant. NOBODY will be able to claim this team of partisan Democrats didn't go the EXTRA 20 MILES looking for ANY evidence they could find of Trump campaign/Russian collusion during the 2016 election

They looked high.

They looked low.

They looked underneath every rock, behind every tree, into every bush.

And they found...NOTHING.

Those saying Mueller will file obstruction charges against Trump: laughable.

What documents did Trump tell the Mueller team it couldn't have? What witnesses were withheld and never interviewed?

THERE WEREN'T ANY.

Mueller got full 100% cooperation as the record will show.