## Sunday, January 18, 2009

### Thoughts on Understanding Neural Networks

Lately, I've been thinking quite a bit about neural networks. In particular, I've been wondering whether it is actually possible to understand them. As a note, this posting assumes that the reader has some understanding of neural networks. Of course, we at Data Miners, heartily recommend our book Data Mining Techniques for Marketing, Sales, and Customer Relationship Management for introducing neural networks (as well as a plethora of other data mining algorithms).

Let me start with a picture of a neural network. The following is a simple network that takes three inputs and has two nodes in the hidden layer:

Note that this structure of the network explains what is really happening. The "input layer" (the first layer connected to the inputs) standardizes the inputs. The "output layer" (connect to the output) is doing a regression or logistic regression, depending on whether the target is numeric or binary. The hidden layers are actually doing a mathematical operation as well. This could be the logistic function; more typically, though it is the hyperbolic tangent. All of the lines in the diagram have weights on them. Setting these weights -- plus a few others not shown -- is the process of training the neural network.

The topology of the neural network is specifically how SAS Enterprise Miner implements the network. Other tools have similar capabilities. Here, I am using SAS EM for three reasons. First, because we teach a class using this tool, I have pre-built neural network diagrams. Second, the neural network node allows me to score the hidden units. And third, the graphics provide a data-colored scatter plot, which I use to describe what's happening.

There are several ways to understand this neural network. The most basic way is "it's a black box and we don't need to understand it." In many respects, this is the standard data mining viewpoint. Neural networks often work well. However, if you want a technique that let's you undersand what it is doing, then choose another technique, such as regression or decision trees or nearest neighbor.

A related viewpoint is to write down the equation for what the network is doing. Then point out that this equation *is* the network. The problem is not that the network cannot explain what it is doing. The problem is that we human beings cannot understand what it is saying.

I am going to propose two other ways of looking at the network. One is geometrically. The inputs are projected onto the outputs of the hidden layer. The results of this projection are then combined to form the output. The other method is, for lack of a better term, "clustering". The hidden nodes actually identify patterns in the original data, and one hidden node usually dominates the output within a cluster.

Let me start with the geometric interpretation. For the network above, there are three dimensions of inputs and two hidden nodes. So, three dimensions are projected down to two dimensions.

I do need to emphasize that these projections are not the linear projections. This means that they are not described by simple matrices. These are non-linear projections. In particular, a given dimension could be stretched non-uniformly, which further complicates the situation.

I chose two nodes in the hidden layer on purpose, simply because two dimensions are pretty easy to visualize. Then I went and I tried it on a small neural network, using Enterprise Miner. The next couple of pictures are scatter plots made with EM. It has the nice feature that I can color the points based on data -- a feature sadly lacking from Excel.

The following scatter plot shows the original data points (about 2,700 of them). The positions are determined by the outputs of the hidden layers. The colors show the output of the network itself (blue being close to 0 and red being close to 1). The network is predicting a value of 0 or 1 based on a balanced training set and three inputs.

Hmm, the overall output is pretty much related to the H1 output rather than the H2 output. We see this becasuse the color changes primarily as we move horizontally across the scatter plot and not vertically. This is interesting. It means that H2 is contributing little to the network prediction. Under these particular circumstances, we can explain the output of the neural network by explaining what is happening at H1. And what is happening at H1 is a lot like a logistic regression, where we can determine the weights of different variables going in.

Note that this is an approximation, because H2 does make some contribution. But it is a close approximation, because for almost all input data points, H1 is the dominant node.

This pattern is a consequence of the distribution of the input data. Note that H2 is always negative and close to -1, whereas H1 varies from -1 to 1 (as we would expect, given the transfer function). This is because the inputs are always positive and in a particular range. The inputs do not result in the full range of values for each hidden node. This fact, in turn, provides a clue to what the neural network is doing. Also, this is close to a degenerate case because one hidden unit is almost always ignored. It does illustrate that looking at the outputs of the hidden layers are useful.

This suggests another approach. Imagine the space of H1 and H2 values, and further that any combination of them might exist (do remember that because of the transfer function, the values actually are limited to the range -1 to 1). Within this space, which node dominates the calculation of the output of the network?

To answer this question, I had to come up with some reasonable way to compare the following values:
• Network output: exp(bias + a1*H1 + a2*H2)
• H1 only: exp(bias + a1*H1)
• H2 only: exp(bias + a2*H2)
Let me give an example with numbers. For the network above, we have the following when H1 and H2 are both -1:
• Network output: 0.9994
• H1 only output: 0.9926
• H2 only output: 0.9749
To calculate the contribution of H1, I use the ratio of the sums of the squares of the differences, as in the following example for H1:
• H1 contribution: (0.9994 - 0.9926)^2 / ((0.9994 - 0.9926)^2 + (0.9994 - 0.9749)^2)
The following scatter plot shows the regions where H1 dominates the overall prediction of the network using this metric (red is H1 is dominant; blue is H2 is dominant):

There are four regions in this scatter plot, defined essentially by the intersection of two lines. In fact, each hidden node is going to add another line on this chart, generating more regions. Within each region, one node is going to dominate. The boundaries are fuzzy. Sometimes this makes no difference, because the output on either side is the same; sometimes it does make a difference.

Note that this scatter plot assumes that the inputs can generate all combinations of values from the hidden units. However, in practice, this is not true, as shown on the previous scatter plot, which essentially covers only the lowest eights of this one.

With the contribution metric, we can then say that for different regions in the hidden unit space, different hidden units dominate the output. This is essentially saying that in different areas, we only need one hidden unit to determine the outcome of the network. Within each region, then, we can identify the variables used by the hidden units and say that they are determining the outcome of the network.

This idea leads to a way to start to understand standard multilayer perceptron neural networks, at least in the space of the hidden units. We can identify the regions where particular hidden units dominate the output of the network. Within each region, we can identify which variables dominate the output of that hidden unit. Perhaps this explains what is happening in the network, because the input ranges limit the outputs only to one region.

More likely, we have to return to the original inputs to determine which hidden unit dominates for a given combination of inputs. I've only just started thinking about this idea, so perhaps I'll follow up in a later post.

--gordon

## Wednesday, January 14, 2009

### Neural Network Training Methods

Scott asks . . .

Dear Ask a Data Miner,

I am using SPSS Clementine 12. The Neural Network node in Clementine allows users to choose from six different training methods for building neural network models:

• Quick. This method uses rules of thumb and characteristics of the data to choose an appropriate shape (topology) for the network.

• Dynamic. This method creates an initial topology but modifies the topology by adding and/or removing hidden units as training progresses.

• Multiple. This method creates several networks of different topologies (the exact number depends on the training data). These networks are then trained in a pseudo-parallel fashion. At the end of training, the model with the lowest RMS error is presented as the final model.

• Prune. This method starts with a large network and removes (prunes) the weakest units in the hidden and input layers as training proceeds. This method is usually slow, but it often yields better results than other methods.

• RBFN. The radial basis function network (RBFN) uses a technique similar to k-means clustering to partition the data based on values of the target field.

• Exhaustive prune. This method is related to the Prune method. It starts with a large network and prunes the weakest units in the hidden and input layers as training proceeds. With Exhaustive Prune, network training parameters are chosen to ensure a very thorough search of the space of possible models to find the best one. This method is usually the slowest, but it often yields the best results. Note that this method can take a long time to train, especially with large datasets.

Which is your preferred training method? How about for a lot of data - (a high number of cases AND a high number of input variables)? How about for a relatively small amount of data?

Scott,

Our general attitude with respect to fancy algorithms is that they provide incremental value. However, focusing on data usually provides more scope for improving results. This is particularly true of neural networks, because stable neural networks should have few inputs.

Before addressing your question, there are a few things that you should keep in mind when using neural networks:

(1) Standardize all the inputs (that is, subtract the average and divide by the standard deviation). This puts all numeric inputs into a particular range.

(2) Avoid categorical inputs! These should be replaced by appropriate numeric descriptors. Neural network tools, such as Clementine, handle categorical inputs using something called n-1 coding, which converts one variable into many flag variables, which, in turn, multiplies the number of weights in the network that need to be optimized.

(3) Avoid variables that are highly collinear. These cause "multidimensional ridges" in the space of neural network weights, which can confuse the training algorithms.

To return to your question in more detail. Try out lots of the different approaches to determine which is best! There is no rule that says that you have to decide on one approach initially and stick with it. To test the approaches use a separate partition of the data to see which works best.

For instance, the Quick method is probably very useful in getting results back in a reasonable amount of time. Examine the topology, though, to see if it makes sense (no hidden units or too many hidden units). Most of the others are all about adding or removing units, which can be valuable. However, always test the methods on a test set that is not used for training. The topology of the network may depend on the training set, so that provides an opportunity for overfitting.

These methods are focusing more on the topology than on the input parameters. If the prune method really does remove inputs, then that would be powerful functionality. For the methods that are comparing results, ensure that the results are compared on a validation set, separate from the test set used to calculate the weights. It can be easy to overfit neural networks, particularly as the number of weights increases.

A comment about the radial basis function approach. Make sure that Clementine is using normalized radial basis functions. Standard neural networks use an s-shaped function that starts low and goes high (or vice versa), meaning that the area under the curve is unbounded. RBFs start low, go high, and then go low again, meaning that the area under the curve is finite. Normalizing the RBFs ensures that the basis functions do not get too small.

My personal favorite approach to neural networks these days is to use principal components as inputs into the network. To work effectively, this requires some background in principal components to choose the right number as inputs into the network.

--gordon

## Friday, January 9, 2009

### Multidimensional Chi-Square, Expected Values, Independence, and All That, Part 3

This post is a continuation of my previous post on extending the chi-square test to more than two dimensions. The standard, two-dimensional chi-square test is explained in Chapter 3 of my book Data Analysis Using SQL and Excel.

This post explains how to implement a multidimensional chi-square test using SQL queries by calculating the chi-square value.

For the purpose of demonstrating this, I will use data derived from the companion web site for Data Analysis Using SQL and Excel. The following query produces data with three dimensions:

`CREATE TABLE d3 as..SELECT paymenttype, MONTH(orderdate) as mon,.........LEFT(zipcode, 1) as zip1, COUNT(*) as cnt..FROM orders..GROUP BY 1, 2, 3`

The table d3 simply contains three dimensions: the payment type, the month of the order date, and the first digit of the zip code. These dimensions are for illustration purposes.

The formula for the expected values is ratio of the following quantities:
• The product of the sum of the counts along each dimension.
• The total sum of the counts to the power of the number of dimensions minus 1.
These quantities can be calculated using basic SQL commands. The following query calculates all the expected values:

`SELECT paymenttype, mon, zip1,.......(dim1.cnt * dim2.cnt * dim3.cnt)/(dimall.cnt*dimall.cnt) as expectedFROM (SELECT paymenttype, SUM(cnt) as cnt......FROM d3......GROUP BY paymenttype) dim1 CROSS JOIN.....(SELECT mon, SUM(cnt) as cnt......FROM d3......GROUP BY mon) dim2 CROSS JOIN.....(SELECT zip1, SUM(cnt) as cnt......FROM d3......GROUP BY zip1) dim3 CROSS JOIN.....(SELECT SUM(cnt) as cnt......FROM d3) dimall`

This query consists of four subqueries, one for each dimension and one for the total count. Each subquery calculates the appropriate sums along one (or no) dimensions. The results themselves are combined using CROSS JOIN, to ensure that the query returns results for all possible combinations of dimensions -- even those combinations that do not appear in the original data.
This latter point is an important point. Expected values are produced even for combinations not in the original data.

The previous query calculates the expected values. However, the chi-square calculation requires a bit more work. One approach is to join the above query to the original table, using a LEFT OUTER JOIN to ensure that no expected values are missing. The following approach uses simple JOINs and assumes that the original table has all combinations of the dimensions.

`SELECT paymenttype, mon, zip1, expected, dev,.......dev*dev/expected as chi_squareFROM (SELECT d3.paymenttype, d3.mon, d3.zip1,.............(dim1.cnt * dim2.cnt * dim3.cnt)/(dimall.cnt*dimall.cnt) as expected,.............d3.cnt-(dim1.cnt * dim2.cnt * dim3.cnt)/(dimall.cnt*dimall.cnt) as dev......FROM d3 JOIN.....(SELECT paymenttype, SUM(cnt) as cnt......FROM d3......GROUP BY paymenttype) dim1.....ON d3.paymenttype = dim1.paymenttype JOIN.....(SELECT mon, SUM(cnt) as cnt......FROM d3......GROUP BY mon) dim2.....ON d3.mon = dim2.mon JOIN.....(SELECT zip1, SUM(cnt) as cnt......FROM d3......GROUP BY zip1) dim3.....ON d3.zip1 = dim3.zip1 CROSS JOIN.....(SELECT SUM(cnt) as cnt......FROM d3) dimall) a`

This query joins in each of the subtotals along the dimensions, rather than using the CROSS JOIN to create all combinations. I suspect that in many databases, this approach has a more efficient execution plan (particularly if there are indexes on the dimensions). Note that the overall total is included using CROSS JOIN. I find this a convenient way to include constants in queries.

This query produces the chi-square value for each cell. The overall chi-square is the sum of these values. To interpret this value, we need the number of degrees of freedom, which is the product of the number of different values on each dimension minus one:

`SELECT (COUNT(DISTINCT paymenttype) - 1)* .......(COUNT(DISTINCT mon) - 1) *.......(COUNT(DISTINCT zip1) - 1) as dofFROM d3`

Interpreting the value itself requires going outside the world of SQL, since there is no function that converts the chi-square value into a p-value within SQL. However, Excel does have such a function, CHIDIST().

It should be obvious how to extend these queries for larger numbers of dimensions. As discussed earlier, though, the chi-square test becomes less useful in multiple dimensions, especially since there need to be counts for all combinations of dimensions for best results (the heuristic rule is a minimum expected value of 5 in all cells). Nevertheless, doing the calculation in multiple dimensions is not difficult, and most of the work can be accomplished using basic SQL queries.