What is elbow method?:
Elbow method So elbowing is this mechanism of
social reiforcement/communication about something that is generally considered bad to say
aloud or is too subtle to try to find words for.
Okay, just kidding, while that’s kinda true, I was just pranking on y’all. What I want to
talk about is a stats/math/Machine Learning method used when trying to find clusters in a
given dataset. So [Elbow Method] (https://en.wikipedia.org/wiki/Elbow_method_(clustering))
is basically a measure/method for interpretation and validation of conistency of a cluster.
Ugh.. the original sentence in Wikipedia is so long with all 10-letter words, I couldn’t
even type it again.(Above attempt was simplified during typing-on-the-fly)
The basic issue is that, during a cluster analysis we need to settle on a few things:
* A measure for distance within, across and between clusters and points in the
- A method/algorithm for updating, re-assigning the points to clusters.
- Optional: A formula for guessing the number of algorithms. In most cases this is
optional, and parameterized.
In the case of elbow method it is a visual method for the third option. Basically, it’s a
ratio of variance (within clusters) divided by overall variance. So it explains how much(or
the total variance is explained by choosing “n” number of clusters.
The name elbow method comes from visually plotting the number of clusters Vs the ratio(% of
variance explained) and finding that point where there’s an acute bend(if no.of.clusters is
in X-axis), picking the number of clusters at that point.
The world around us redunds with opportunities, explodes with opportunities, which nearly all folk ignore because it would require them to violate a habit of thought; in every battle a thousand Hufflepuff bones waiting to be sharpened into spears. If you had thought to try a massed Finite Incantatem on general principles, you would have dispelled Mr. Potter’s suit of chainmail and everything else he was wearing except his underwear, which leads me to suspect that Mr. Potter did not quite realize his own vulnerability. Or you could have had your soldiers swarm Mr. Potter and Mr. Longbottom and physically wrest the wands from their hands. Mr. Malfoy’s own response was not what I would term well-reasoned, but at least he did not wholly ignore his thousand alternatives.” A sardonic smile. “But you, Miss Granger, had the misfortune to remember how to cast the Stunning Hex, and so you did not search your excellent memory for a dozen easier spells that might have proved efficacious. And you pinned all your army’s hopes on your own person, so they lost spirit when you fell. Afterward they continued to cast their futile Sleep Hexes, governed by the habits of fighting that had been trained into them, unable to break the pattern as Mr. Malfoy did. I cannot quite comprehend what goes through people’s minds when they repeat the same failed strategy over and over, but apparently it is an astonishingly rare realization that you can try something else. And so the Sunshine Regiment was wiped out by two soldiers.” The Defense Professor grinned mirthlessly. “One perceives certain similarities to how fifty Death Eaters dominated all of magical Britain, and how our much-loved Ministry continues in its rul
We’ve already seen what F-score is. Now let’s see what
F-test. Side note: I came across it when I was writing
Elbow Method and my thoughts were, cool another F-word for my readers, so
Here you go:
F-test is any stats test that uses F-distribution
It is often used when comparing stats models that have been fitted to a data set.. Ahh.. That
sounds no different from F-score then.. May be just different
fields(Statistics and Machine Learning) have different naming conventions?? Anyway two different
F-words.. So let’s just say what F-score/test?? Why two names for samething and move on…
Null Hypothesis: Means of a given set of normally distributed populations all having same standard deviation being equal.(used in ANOVA)
The hypothesis that a proposed regression model fits the data well.
The hypothesis that a data set in a regression analysis follows the simpler of two proposed linear models that are nested within each other.
It(non-regression type) is also a test of homoskedasticity
A visualization grammar, a language for:
* — creating, saving and sharing interactive visualization designs
* — describe visual appearance and interactive behaviour of visualization in json
* — reactive signals that dynamically modify a visualization in response to input
The key semantics are:
* — width, height, padding, autosize (all are for specifying the size of the
* — data (an array of data definitions, can define type, name, stream, url, and values of the
— scales (Configurations for as to map columns of data to pixel positions or
colors, or type of representation(for ex: categorical==> bands etc)).
— axes (Configuration of axes)
— marks (Graphical primitives, which are used to encode data. Has properties
position, size, shape, color. Examples are: dot, circle, rectangle(bar-chart),
- — Have sub properties encode which marks the graphical primitives
- — Encode’s Sub property enter and exit configure interactive parts when
the mark is added or removed.
- — marks sub property hover, update configure overall interactive parts
- — each of the hover, update properties can be triggered/linked to signals
and changed accordingly
— A special type of mark called group is present and can contain other
marks(for composition of graphical primitives to create complex ones)
— signals (act as dynamic variables, or as event-listeners to use js parlance)
- — Has sub property event streams
- — Can set dynamically evaluated variables as values on events as
- — Events can be mouse over, mouse out, click,drag etc..
- — Event streams
- — Has sub properties source, type, marktype, between, consume, filetr etc.
- — Each sub property decides which mark to change/update, based on which
— Event streams also have CSS-style selectors
- — Can create legends for the visualizatinos
— customize them with sub properties type, orient, fill, opacity, shape
- — As the name implies it can transform data streams
- — Has sub properties ilke filter, stack, aggregate, bin, collect, fold,
There was a legendary episode in social psychology called the Robbers Cave experiment. It had been set up in the bewildered aftermath of World War II, with the intent of investigating the causes and remedies of conflicts between groups. The scientists had set up a summer camp for 22 boys from 22 different schools, selecting them to all be from stable middle-class families. The first phase of the experiment had been intended to investigate what it took to start a conflict between groups. The 22 boys had been divided into two groups of 11 –
– and this had been quite sufficient.
The hostility had started from the moment the two groups had become aware of each others’ existences in the state park, insults being hurled on the first meeting. They’d named themselves the Eagles and the Rattlers (they hadn’t needed names for themselves when they thought they were the only ones in the park) and had proceeded to develop contrasting group stereotypes, the Rattlers thinking of themselves as rough-and-tough and swearing heavily, the Eagles correspondingly deciding to think of themselves as upright-and-proper.
The other part of the experiment had been testing how to resolve group conflicts. Bringing the boys together to watch fireworks hadn’t worked at all. They’d just shouted at each other and stayed apart. What had worked was warning them that there might be vandals in the park, and the two groups needing to work together to solve a failure of the park’s water system. A common task, a common enemy.
Harry had a strong suspicion Professor Quirrell had understood this principle very well indeed when he had chosen to create three armies per year.
Hermione. “I’m not angry at you,” Harry said. His voice was cold, despite his best efforts. “I’m angry at, I don’t know, everything. But I’m not willing to lose, Hermione. Losing isn’t always the right thing to do. I’ll figure out how to do something a grown wizard can’t do, and then I’ll get back to you. How’s that?”
I am traveling by kallada travels today to my hometown. It’s a nonA/C sleeper. And it is expected to leave Madiwala by 8.45 pm. It left around 9.00 pm…Hmm play 15 minutes delay is understood with Bangalore traffic.
However we’ve been waiting at Chandapura for an hour for two more passengers…It is currently 23:16 hrs and they are now moving after picking up those 2 passengers..
This is just fucking lousy, careless, arrogant service. All questions to the guys (apparently in-charge) are deflected with a boss or manager says so…
Never taking these travels again..And redBus might as well block these travels.
Over the last couple of years, I’ve been dabbling, and really just buying on impulse and random
reading online stock tips and forums. At the year-end while filing taxes and tallying up I realized(not
surprisingly, I might add) I’ve lost money(Thanks to the bull market, only little).
Which is when I realized, I’ve been half-assing the amount of research, I should do before investing
in stock market, and what’s worse, I’ve been falling prey to the fallacy ” a little knowledge is a
So this is an attempt to hide the crime and in the process, build a system to avoid committing the
crime in the future.
Before I begin, some of the sources, I’ve been half-assing for research,(but good sources
This is a first of a series of posts:
Most of the data, I used(and will use for the series) in the following analysis was picked up from investr(thanks to r/hapuchu,
for sharing the data), but can be picked up by crawling the webpages of companise for quarterly
and/or annual reports, and then parsing the pdf to consume them.
Some Caveats and Exceptions:
- –I’m writing this around the end/last week of february.
- — These are all stocks I traded in starting in 2nd half of 2015.
- — I’ve done some stock investment during the 2006-2009, made some money, but due to
bad(nah had no clue about it)
portfolio/cashflow management, had to sell a bunch of them in 2009, which put overall
returns negative and stopped trading, leaving whatever was left. But learnt the lesson, not to put any amount of money I’m not ok with losing into stocks.
— I’m working in the IT industry, and have spent some spare-time reading Finance, but
nowhere near dedicated or focussed. (Not sure that kinda reading is good.)
— Most of the energy stocks are from when I decided I’ll go thematic on renewable energy
and bought them, but lost patience/nerve when the stocks went down and eventually sold off
Direct-Equity[^1] Portfolio Opinions:
- Way too many stocks
- Way too disorganized and unfocused and under researched
- Not enough Focus on long-term(think companies that’ll stay for > 100 years)
- Balance long-term (black-swan)focus with dividend-based focus(For ex this)
Ok here’s a list of the stocks I’ve traded:
- I’ve seen the share price of this it has been hovering around 1000 for the
10 years I’ve seen this stock, so this can be part of a stable portfolio.
- This is absurd, as for all I know, the stock could have split 10 times in
those 10 years, which means the stock has risen or merged, which means it
has fallen. I haven’t checked it but the point is that it is a fallacy.
- Not much, just that I’ve like Infy stocks in the past, and they seemed to
be on a downtick
- Well it’s just an impulse buy, and not better than gambling.
- It has recovered and got back to trading better, but that’s just luck.
- Can’t even remember, where but it was some analyst rating and read
- Belief in expert fallacy.
- Loss. Panicked and sold at 200. It seems to be doing a little better, but
even now it would be a loss for me to sell, however the system of
investment was wrong.
- Can’t even remember, where but it was blog/reddit thread
- Belief in expert fallacy.
- Belief in crowd decision fallacy??
- Up by about 20% lucky bull ride
* Up by about 1/8th
* Lost a bit.
* Lost money
* Gain a little bit
- I’ve had good experience buying it during the IPO and making money(
- I’ve a bias for the non-renewable sector ‘s future prospects.
- Using inductive reasoning when there’s no reason(IPO is different from regular trading)
- Prior Bias (Ideally should have built a prediction, and accounted for non-renewable energy’s future bias I have)
* Lost a fair amount of money
- Building a dividend portfolio and saw good ratings about it on investr’s magic formula
- Bought a scooter and decided, I could buy some auto stocks
- I was in a automobile theme, and maruti has a big brand in India
- Also was thinking of future plans for a car, and maruti was an automatic pick
- It has a relatively higher P/E
- Was in a automobile theme,
- Bought a TVS Scooter
- Shorten the number of stocks and focus the money into a few
- Build a internal system for analyzing companies before invest in the future
[^1] — I might eventually broaden the scope of blog posts, but don’t expect it for a looong, loong time(count in decades)…