We are in the last stages of proofing the galleys/typesetting of Zumel, Mount, Practical Data Science with R, 2nd Edition, Manning 2019. So this edition will definitely be out soon!
If you ever wanted to see what Nina Zumel and John Mount are like when we have the help of editors, this book is your chance!
One thing I noticed in working through the galleys: it becomes easy to see why Dr. Nina Zumel is first author.
2/3rds of the book is her work.
We are excited to share a free extract of Zumel, Mount, Practical Data Science with R, 2nd Edition, Manning 2019: Evaluating a Classification Model with a Spam Filter.
This section reflects an important design decision in the book: teach model evaluation first, and as a step separate from model construction.
It is funny, but it takes some effort to teach in this way. New data scientists want to dive into the details of model construction first, and statisticians are used to getting model diagnostics as a side-effect of model fitting. However, to compare different modeling approaches one really needs good model evaluation that is independent of the model construction techniques.
This teaching style has worked very well for us both in R and in Python (it is considered one of the merits of our LinkedIn AI Academy course design):
(Note: Nina Zumel, leads on the course design, which is the heavy lifting, John Mount just got tasked to be the one delivering it.)
Zumel, Mount, Practical Data Science with R, 2nd Edition is coming out in print very soon. Here is a discount code to help you get a good deal on the book:
Take 37% off Practical Data Science with R, Second Edition by entering fcczumel3 into the discount code box at checkout at manning.com.
Nina Zumel finished new documentation on how vtreat‘s cross validation works, which I want to share here.
vtreat is a system that makes data preparation for machine learning a “one-liner” (available in R or available in Python). We have a set of starting off points here. These documents describe what vtreat does for you, you just find the one that matches your task and you should have a good start for solving data science problems in R or in Python.
The latest documentation is a bit about how vtreat works, and how to control some of the details of the work it is doing for you.
The new documentation is:
Please give one of the examples a try, and consider adding vtreat to your data science workflow.
To understand computations in R, two slogans are helpful:
- Everything that exists is an object.
- Everything that happens is a function call.
John Chambers
In R, the “[” array access operator is a function call. And it is one a user can re-bind to the new effect of their own choosing.
Let’s see what sort of mischief we can get into using this capability.
Nina Zumel finished some great new documentation showing how to use Python vtreat to prepare data for multinomial classification mode. And I have finally finished porting the documentation to R vtreat. So we now have good introductions on how to use vtreat to prepare data for the common tasks of:
R regression example, Python regression example.R classification example, Python classification example.R unsupervised example, Python unsupervised example.R multinomial classification example, Python multinomial classification example.That is now 8 introductions to start with. To use vtreat you only have to work through one introduction (the one helping with the task you have at hand in the language you are using).
As I have said before:
vtreat helps with project blocking issues commonly seen in real world data: missing values, re-coding categorical variables, and dealing high cardinality categorical variables.vtreat in your data science projects: you are really missing out (and making more work for yourself).When studying regression models, One of the first diagnostic plots most students learn is to plot residuals versus the model’s predictions (that is, with the predictions on the x-axis). Here’s a basic example.
# build an "ideal" linear process.
set.seed(34524)
N = 100
x1 = runif(N)
x2 = runif(N)
noise = 0.25*rnorm(N)
y = x1 + x2 + noise
df = data.frame(x1=x1, x2=x2, y=y)
# Fit a linear regression model
model = lm(y~x1+x2, data=df)
summary(model)##
## Call:
## lm(formula = y ~ x1 + x2, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.73508 -0.16632 0.02228 0.19501 0.55190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16706 0.07111 2.349 0.0208 *
## x1 0.90047 0.09435 9.544 1.30e-15 ***
## x2 0.81444 0.09288 8.769 6.07e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2662 on 97 degrees of freedom
## Multiple R-squared: 0.6248, Adjusted R-squared: 0.6171
## F-statistic: 80.78 on 2 and 97 DF, p-value: < 2.2e-16# plot it
library(ggplot2)
df$pred = predict(model, newdata=df)
df$residual = with(df, y-pred)
# standard residual plot
ggplot(df, aes(x=pred, y=residual)) +
geom_point(alpha=0.5) + geom_hline(yintercept=0, color="red") +
geom_smooth(se=FALSE) +
ggtitle("Standard residual plot",
subtitle = "linear model and process")
In the above plot, we’re plotting the residuals as a function of model prediction, and comparing them to the line y = 0, using a smoothing curve through the residuals. The idea is that for a well-fit model, the smoothing curve should approximately lie on the line y = 0. This is true not only for linear models, but for any model that captures most of the explainable variance, and for which the unexplainable variance (the noise) is IID and zero mean.
If the residuals aren’t zero mean independently of the model’s predictions, then either you are missing some explanatory variables, or your model does not have the correct structure, or an appropriate inductive bias. A simple example of the second case is trying to fit a linear model to a process where the outcome is quadratically (or otherwise non-linearly) related to the outcome. To see this, let’s make an example quadratic system while deliberately failing to supply that structure to the model.
# a simple quadratic example
x3 = runif(N)
qf = data.frame(x1=x1, x2=x2, x3=x3)
qf$y = x1 + x2 + 2*x3^2 + 0.25*noise
# Fit a linear regression model
model2 = lm(y~x1+x2+x3, data=qf)
# summary(model2)
qf$pred = predict(model2, newdata=qf)
qf$residual = with(qf, y-pred)
ggplot(qf, aes(x=pred, y=residual)) +
geom_point(alpha=0.5) + geom_hline(yintercept=0, color="red") +
geom_smooth(se=FALSE) +
ggtitle("Standard residual plot",
subtitle = "linear model, quadratic process")
In this case, the smoothing line on the residuals doesn’t approximate the line y = 0; when the model predicts a value in the range 1 to about 2.3, it tends to be overpredicting; otherwise, it tends to underpredict. This is an instance of a pathology called “structure in the residuals.”
What happens if you erroneously plot the residuals versus the true outcome, instead of the predictions? Let’s try this with the model for the linear process (which we know is a well-fit model):
# the wrong residual graph
ggplot(df, aes(x=y, y=residual)) +
geom_point(alpha=0.5) + geom_hline(yintercept=0, color="red") +
geom_smooth(se=FALSE) +
ggtitle("Incorrect residual plot",
subtitle = "linear model and process")
If you make this plot when you meant to make the other, you will give yourself a nasty shock. Plotting residuals versus the outcome will always look more or less like the above graph. You might think that for a good model, the outcome and the prediction are close to each other, so the residual graphs should look about the same no matter which quantity you plot on the x-axis, right? Why do they look so different?
One reason that the proper residual graph (for a well fit model) should smooth out to the line y=0 is known as reversion to mediocrity, or regression to the mean.
Imagine that you have an ideal process that always produces a single value y. You don’t actually observe this “true value”; instead, what you observe is y plus (IID, zero mean) noise. You can build a “model” for this process that predicts the mean of the observations, in this case the value 0.1033149. Then you can calculate the residuals of your “model” in the usual way.
When you plot the residuals as a function of the prediction, all the datums fall at the same horizontal coordinate of the graph, centered around zero, and approximately equally distributed between positive and negative. The “smoothing line” through this graph is simply the point (0.1033149, 0) – that is, the graph is centered at zero.
On the other hand, if you plot the residuals as a function of the observed outcome, all the observations will be sorted so that the observations with positive noise are to the right of the observations with negative noise, and the smoothing line through the graph no longer looks like the line y = 0.
For a process that varies as a function of the input, you can think of the prediction corresponding to an input X as the mean of all the observations corresponding to X, and the idea is the same.
Incidentally, this regression to the mean is also why model predictions tend to have less range than the original training data.
Sometimes instead of plotting residuals versus the predictions, I plot observations versus predictions. In this case, you want to check that the predictions lie approximately on the line y = x. This isn’t a standard diagnostic plot, but it does give a better sense of the magnitude of the errors relative to the magnitudes of the outcomes. Again, the important thing to remember is that the predictions go on the x-axis.
Here’s the correct plot:
# standard prediction plot
ggplot(df, aes(x=pred, y=y)) +
geom_point(alpha=0.5) + geom_abline(color="red") +
geom_smooth(se=FALSE) +
ggtitle("Standard prediction plot")
And here’s the wrong plot:
# the "wrong" way
ggplot(df, aes(x=y, y=pred)) +
geom_point(alpha=0.5) + geom_abline(color="red") +
geom_smooth(se=FALSE) +
ggtitle("Incorrect prediction plot")
Notice how the wrong plot again seems to show pathological structure where none exists.
The above examples show why you should always take care to plot your model diagnostics as functions of the predictions and not of the observations. Most students have heard this already, but we feel that demonstrating why will be more memorable that simply saying “make it so.”
Real world data can present a number of challenges to data science workflows. Even properly structured data (each interesting measurement already landed in distinct columns), can present problems, such as missing values and high cardinality categorical variables.
In this note we describe some great tools for working with such data.
Nina Zumel has been polishing up new vtreat for Python documentation and tutorials. They are coming out so good that I find to be fair to the R community I must start to back-port this new documentation to vtreat for R.
Continue reading Preparing Data for Supervised Classification
Nina Zumel had a really great article on how to prepare a nice Keras performance plot using R.
I will use this example to show some of the advantages of cdata record transform specifications.
Continue reading The Advantages of Record Transform Specifications
Just got the following note from a new reader:
Thank you for writing Practical Data Science with R. It’s challenging for me, but I am learning a lot by following your steps and entering the commands.
Wow, this is exactly what Nina Zumel and I hoped for. We wish we could make everything easy, but an appropriate amount of challenge is required for significant learning and accomplishment.
Of course we try to avoid inessential problems. All of the code examples from the book can be found here (and all the data sets here).
The second edition is coming out very soon. Please check it out.