Skip to main content

Why does Kaggle use Log-loss?

A Very Rough Guide to Getting Started in Data Science: Part II, The Big Picture

Data science to a beginner seems completely overwhelming. Not only are there huge numbers of programming languages, packages and algorithms, but even managing your data is an entire area itself. Some examples are the languages R, Python, Ruby, Perl, Julia, Mathematica, MATLAB/Octave; packages SAS, STATA, SPSS; algorithms linear regression, logistic regression, nested model, neural nets, support vector machines, linear discriminant analysis and deep learning.
For managing your data some people use Excel, or a relational database like MySQL or PostgreSQL. And where do things like big data, NoSQL and Hadoop fit in? And what's gradient descent and why is it important? But perhaps the most difficult part of all is that you actually need to know and understand statistics, too.

It does seem overwhelming, but there's a simple key idea - data science is using data to answer a question. Even if you're only sketching a graph using a stick and a sandbox, you're still doing data science. Your goal for data science should be to continually learn better, more powerful and more efficient ways to answer your questions. My general framework has been strongly influenced by George Pólya's wonderful book "How to Solve It". While it's directed at solving mathematical problems, his approach is helpful for solving problems in general.

"How to Solve It" suggests the following steps when solving a mathematical problem:
  1. First, you have to understand the problem.
  2. After understanding, then make a plan.
  3. Carry out the plan.
  4. Review/extend. Look back on your work. How could it be better?
Pólya goes into much greater detail for each step and provides some illustrative examples. It's not the final word on how to approach and solve mathematical problems, but it's very helpful and I highly recommend it. For data science, the analogous steps from my perspective would be:
  1. What questions do you want to answer?
  2. What data would be helpful to answer these questions? How and where do you get this data?
  3. Given the question you want to answer and the data you have, which approaches and models are likely to be useful? This can be very confusing. There are always tradeoffs - underfitting vs overfitting, bias vs variance, simplicity vs complexity, information about where something came from vs what's it doing
  4. Perform analysis/fit model.
  5. How do you know if your model and analysis are good or bad, and how confident should you be in your predictions and conclusions? A very critical, but commonly treated lightly or even skipped entirely.
  6. Given the results, what should you try next?
Let's follow Pólya and do an illustrative example next.

Comments

Popular posts from this blog

A Bayes' Solution to Monty Hall

For any problem involving conditional probabilities one of your greatest allies is Bayes' Theorem. Bayes' Theorem says that for two events A and B, the probability of A given B is related to the probability of B given A in a specific way.

Standard notation:

probability of A given B is written \( \Pr(A \mid B) \)
probability of B is written \( \Pr(B) \)

Bayes' Theorem:

Using the notation above, Bayes' Theorem can be written: \[ \Pr(A \mid B) = \frac{\Pr(B \mid A)\times \Pr(A)}{\Pr(B)} \]Let's apply Bayes' Theorem to the Monty Hall problem. If you recall, we're told that behind three doors there are two goats and one car, all randomly placed. We initially choose a door, and then Monty, who knows what's behind the doors, always shows us a goat behind one of the remaining doors. He can always do this as there are two goats; if we chose the car initially, Monty picks one of the two doors with a goat behind it at random.

Assume we pick Door 1 and then Monty sho…

What's the Value of a Win?

In a previous entry I demonstrated one simple way to estimate an exponent for the Pythagorean win expectation. Another nice consequence of a Pythagorean win expectation formula is that it also makes it simple to estimate the run value of a win in baseball, the point value of a win in basketball, the goal value of a win in hockey etc.

Let our Pythagorean win expectation formula be \[ w=\frac{P^e}{P^e+1},\] where \(w\) is the win fraction expectation, \(P\) is runs/allowed (or similar) and \(e\) is the Pythagorean exponent. How do we get an estimate for the run value of a win? The expected number of games won in a season with \(g\) games is \[W = g\cdot w = g\cdot \frac{P^e}{P^e+1},\] so for one estimate we only need to compute the value of the partial derivative \(\frac{\partial W}{\partial P}\) at \(P=1\). Note that \[ W = g\left( 1-\frac{1}{P^e+1}\right), \] and so \[ \frac{\partial W}{\partial P} = g\frac{eP^{e-1}}{(P^e+1)^2}\] and it follows \[ \frac{\partial W}{\partial P}(P=1) = …

Behind the Speadsheet

In the book "The Only Rule Is It Has to Work: Our Wild Experiment Building a New Kind of Baseball Team", Ben Lindbergh and Sam Miller recount a grand adventure to take command of an independent league baseball team, with the vision of trying every idea, sane or crazy, in an attempt to achieve a winning edge. Five infielders, four outfielders, defensive shifts, optimizing lineups - everything.

It was really an impossible task. Professional sports at every level are filled with highly accomplished and competitive athletes, with real lives and real egos. Now imagine walking in one day and suddenly trying to convince them that they should be doing things differently. Who do you think you are?

I was one of the analysts who helped Ben and Sam in this quest, and I wanted to write some thoughts down from my own perspective, not as one of the main characters, but as someone more behind the scenes. These are some very short initial thoughts only, but I'd like to followup with some…