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Category: Data Science

Gradient Boosting In R

Published 2017-08-28 by Kevin Feasel

Anish Sing Walia walks us through a gradient boosting exercise using R:

An important thing to remember in boosting is that the base learner which is being boosted should not be a complex and complicated learner which has high variance for e.g a neural network with lots of nodes and high weight values.For such learners boosting will have inverse effects.

So I will explain Boosting with respect to decision trees in this tutorial because they can be regarded as weak learners most of the times.We will generate a gradient boosting model.

Click through for more details.  H/T R-Bloggers

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Evaluating A Data Science Project

Published 2017-08-25 by Kevin Feasel

Tom Fawcett gives us an interesting evaluation of a data science case study:

The model is a fully connected neural network with three hidden layers, with a ReLU as the activation function. They state that data from Google Compute Engine was used to train the model (implemented in TensorFlow), and Cloud Machine Learning Engine’s HyperTune feature was used to tune hyperparameters.

I have no reason to doubt their representation choices or network design, but one thing looks odd. Their output is two ReLU (rectifier) units, each emitting the network’s accuracy (technically: recall) on that class. I would’ve chosen a single Softmax unit representing the probability of Large Loss driver, from which I could get a ROC or Precision-Recall curve. I could then threshold the output to get any achievable performance on the curve. (I explain the advantages of scoring over hard classification in this post.)

But I’m not a neural network expert, and the purpose here isn’t to critique their network design, just their general approach. I assume they experimented and are reporting the best performance they found.

Read the whole thing.

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Regression Trees And Double Seasonal Time Series Trends

Published 2017-08-24 by Kevin Feasel

Peter Laurinec walks us through an example of using regression trees to solve a problem with double-seasonal time series data in R:

Classification and regression tree (or decision tree) is broadly used machine learning method for modeling. They are favorite because of these factors:

  • simple to understand (white box)
  • from a tree we can extract interpretable results and make simple decisions
  • they are helpful for exploratory analysis as binary structure of tree is simple to visualize
  • very good prediction accuracy performance
  • very fast
  • they can be simply tuned by ensemble learning techniques

But! There is always some “but”, they poorly adapt when new unexpected situations (values) appears. In other words, they can not detect and adapt to change or concept drift well (absolutely not). This is due to the fact that tree creates during learning just simple rules based on training data. Simple decision tree does not compute any regression coefficients like linear regression, so trend modeling is not possible. You would ask now, so why we are talking about time series forecasting with regression tree together, right? I will explain how to deal with it in more detail further in this post.

This was a very interesting article.  Absolutely worth reading.  H/T R-Bloggers

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K Nearest Cliques

Published 2017-08-18 by Kevin Feasel

Vincent Granville explains an algorithm built around finding cliques of data points:

The cliques considered here are defined by circles (in two dimensions) or spheres (in three dimensions.) In the most basic version, we have one clique for each cluster, and the clique is defined as the smallest circle containing a pre-specified proportion p of the points from the cluster in question. If the clusters are well separated, we can even use p = 1. We define the density of a clique as the number of points per unit area. In general, we want to build cliques with high density.

Ideally, we want each cluster in the training set to be covered by a small number of (possibly slightly overlapping) cliques, each one having a high density.  Also, as a general rule, a training set point can only belong to one clique, and (ideally) to only one cluster. But the circles associated with two cliques are allowed to overlap.

It’s an interesting approach, and I can see how it’d be faster than K Nearest Neighbors, but I do wonder how accurate the results would be in comparison to KNN.

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Building An Image Recognizer With R

Published 2017-08-17 by Kevin Feasel

David Smith has a post showing how to build an image recognizer with R and Microsoft’s Cognitive Services Library:

The process of training an image recognition system requires LOTS of images — millions and millions of them. The process involves feeding those images into a deep neural network, and during that process the network generates “features” from the image. These features might be versions of the image including just the outlines, or maybe the image with only the green parts. You could further boil those features down into a single number, say the length of the outline or the percentage of the image that is green. With enough of these “features”, you could use them in a traditional machine learning model to classify the images, or perform other recognition tasks.

But if you don’t have millions of images, it’s still possible to generate these features from a model that has already been trained on millions of images. ResNet is a very deep neural network model trained for the task of image recognition which has been used to win major computer-vision competitions. With the rxFeaturize function in Microsoft R Client and Microsoft R Server, you can generate 4096 features from this model on any image you provide. The features themselves are meaningful only to a computer, but that vector of 4096 numbers between zero and one is (ideally) a distillation of the unique characteristics of that image as a human would recognize it. You can then use that features vector to create your own image-recognition system without the burden of training your own neural network on a large corpus of images.

Read the whole thing and follow David’s link to the Microsoft Cognitive blog for more details.

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Understanding Decision Trees

Published 2017-08-16 by Kevin Feasel

Ramandeep Kaur explains how decision trees work:

Simply put, a decision tree is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.

It is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems and works for both categorical and continuous input and output variables. It is one of the most widely used and practical methods for Inductive Inference. (Inductive inference is the process of reaching a general conclusion from specific examples.)

Decision trees learn and train itself from given examples and predict for unseen examples.

Click through for an example of implementing the ID3 algorithm and generating a decision tree from a data set.

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Diamond: Solving Generalized Linear Models Using Python

Published 2017-08-11 by Kevin Feasel

Tim Sweester and Aaron Bradley announce Diamond, a Python library which solves certain kinds of generalized linear models.  In a two-part series, they explain more.  Part 1 covers the mathematical principles behind it:

Many computational problems in data science and statistics can be cast as convex problems. There are many advantages to doing so:

  • Convex problems have a unique global solution, i.e. there is one best answer
  • There are well-known, efficient, and reliable algorithms for finding it

One ubiquitous example of a convex problem in data science is finding the coefficients of an L2L2-regularized logistic regression model using maximum likelihood. In this post, we’ll talk about some basic algorithms for convex optimization, and discuss our attempts to make them scale up to the size of our models. Unlike many applications, the “scale” challenge we faced was not the number of observations, but the number of features in our datasets. First, let’s review the model we want to fit.

Part 2 looks at one interesting use case:

In this example, GLMMs allow you to pool information across different brands, while still learning individual effects for each brand. It breaks the problem into sets of fixed and random effects. The fixed effects are similar to what you would find in a traditional logistic regression model, while the random effects allow the regression relationship to vary for each brand. One of the advantages of GLMMs is that they learn how different brands are from each other. Brands that are very similar to the overall average will have small random effect estimates. Because of the regularization of these models, brands with few observations will also have small random effect estimates, and be treated more like the overall average. In contrast, for brands that are very different from the average, with lots of data to support that, GLMMs will learn large random effect estimates.

Check it out.  Part 2 also contains a link to the GitHub repo if you want to try it on your own.

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Understanding K-Means Clustering

Published 2017-08-10 by Kevin Feasel

Chaitanya Sagar has a good explanation of the assumptions k-means clustering makes:

Why do we assume in the first place? The answer is that making assumptions helps simplify problems and simplified problems can then be solved accurately. To divide your dataset into clusters, one must define the criteria of a cluster and those make the assumptions for the technique. K-Means clustering method considers two assumptions regarding the clusters – first that the clusters are spherical and second that the clusters are of similar size. Spherical assumption helps in separating the clusters when the algorithm works on the data and forms clusters. If this assumption is violated, the clusters formed may not be what one expects. On the other hand, assumption over the size of clusters helps in deciding the boundaries of the cluster. This assumption helps in calculating the number of data points each cluster should have. This assumption also gives an advantage. Clusters in K-means are defined by taking the mean of all the data points in the cluster. With this assumption, one can start with the centers of clusters anywhere. Keeping the starting points of the clusters anywhere will still make the algorithm converge with the same final clusters as keeping the centers as far apart as possible.

Read on as Chaitanya shows several examples; the polar coordinate transformation was quite interesting.  H/T R-Bloggers

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Forecasting Versus Predicting

Published 2017-08-09 by Kevin Feasel

Rob Collie explains that there are two different concepts which use similar names:

Once you’ve digested the illustration at the top of this article, yeah, you’ve kind already got it.

  • Forecasting is when we anticipate the behavior of “Lots” of people (customers, typically) on “Long” timelines.
  • Predictive Analytics anticipate the behavior of One person (again, typically a customer) on a “Short” timeline.

So…  Macro versus Micro.

But let’s delve just a little bit deeper, in order to “cement” the concepts.

There’s a very useful distinction here and Rob does well to flesh out the details.  I highly recommend this if you’re curious about micro- versus macro-level predictions.

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Deep Learning Isn’t The End-All Be-All Solution

Published 2017-08-08 by Kevin Feasel

Pablo Cordero explains that deep learning solutions are not the best choice in all cases:

The second preconception I hear the most is the hype. Many yet-to-be practitioners expect deep nets to give them a mythical performance boost just because it worked in other fields. Others are inspired by impressive work in modeling and manipulating images, music, and language – three data types close to any human heart – and rush headfirst into the field by trying to train the latest GAN architecture. The hype is real in many ways. Deep learning has become an undeniable force in machine learning and an important tool in the arsenal of any data modeler. Its popularity has brought forth essential frameworks such as tensorflow and pytorch that are incredibly useful even outside deep learning. Its underdog to superstar origin story has inspired researchers to revisit other previously obscure methods like evolutionary strategies and reinforcement learning. But it’s not a panacea by any means. Aside from lunch considerations, deep learning models can be very nuanced and require careful and sometimes very expensive hyperparameter searches, tuning, and testing (much more on this later in the post). Besides, there are many cases where using deep learning just doesn’t make sense from a practical perspective and simpler models work much better.

It’s a very interesting article, pointing out that deep learning solutions work better than expected on smaller data sizes, but there are areas where it’s preferable to choose something else.

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