### Dummy variable trap neural network

Using categorical data in Multiple Regression Models is a powerful method to include non-numeric data types into a regression model. In a regression model, these values can be represented by dummy variables — variables containing values such as 1 or 0 representing the presence or absence of the categorical value. By including dummy variable in a regression model however, one should be careful of the Dummy Variable Trap.

The Dummy Variable trap is a scenario in which the independent variables are multicollinear — a scenario in which two or more variables are highly correlated; in simple terms one variable can be predicted from the others.

Including a dummy variable for each is redundant of male is 0, female is 1, and vice-versahowever doing so will result in the following linear model:. In the above model, the sum of all category dummy variable for each row is equal to the intercept value of that row — in other words there is perfect multi-collinearity one value can be predicted from the other values.

Intuitively, there is a duplicate category: if we dropped the male category it is inherently defined in the female category zero female value indicate male, and vice-versa. The solution to the dummy variable trap is to drop one of the categorical variables or alternatively, drop the intercept constant — if there are m number of categories, use m-1 in the model, the value left out can be thought of as the reference value and the fit values of the remaining categories represent the change from this reference.

Whoops, the matrix cannot be inverted because it is singular. To fix the issue, we can remove the intercept, or alternatively remove one of the dummy variable columns. The calculated values are now referenced to the dropped dummy variable in this case C1. In other words, if the category is C2 it is In some cases it may be necessary or educational to program dummy variables directly into a model. However in most cases a statistical package such as R can do the math for you — in R categories can be represented by factors, letting R deal with the details:.

Learn for Master. It's never too late to learn to be a master. C1 C2 C3 b. Error in solve. C2 C3 b. Column C is a factor column. Call :. Coefficients :. Shop Amazon Gift Cards. Any Occasion.

No Expiration. Best books to master algorithms. Subscribe E-mail Address:. Unsubscribe me. Categories All categories. Big Data.Training an artificial neural network involves the following steps:. An insurance company has approached you with a dataset of previous claims of their clients. The insurance company wants you to develop a model to help them predict which claims look fraudulent. By doing so you hope to save the company millions of dollars annually.

This is a classification problem. These are the columns in our dataset. As in many business problems, the data provided will not be processed for us.

We therefore have to prepare it in a way that our algorithm will accept it. We see from the dataset that we have some categorical columns.

Regression 5: Multicollinearity & Dummy Variables

We need to convert these to zeros and ones so that our deep learning model will be able to understand them. Another thing to note is that we have to feed our dataset to the model as numpy arrays. Below we import the necessary packages and then load in our dataset.

We then to convert the categorical columns to dummy variables. For example, if you have a, b, c, d as categories then you can drop d as a dummy variable.

This is referred to as multicollinearity. We must avoid using the same dataset to train and test the model. We set.

This is the way our deep learning model will accept the data. This step is important because our machine learning model expects the data in form of arrays. We then split the data into a training and test set. We use 0. Due to the massive amounts of computations taking place in deep learning, feature scaling is compulsory. Feature scaling standardizes the range of our independent variables. Fritz AI has the developer tools to make this transition possible. The first thing we need to do is import Keras.

By default, Keras will use TensorFlow as its backend.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

I know that categorical data should be one-hot encoded before training the machine learning algorithm. I also need that for multivariate linear regression I need to exclude one of the encoded variable to avoid so called dummy variable trap. Ex: If I have categorical feature "size": "small", "medium", "large", then in one hot encoded I would have something like:.

So to avoid dummy variable trap I need to remove any of the 3 columns, for example, column "small". Should I do the same for training a Neural Network? Or this is purely for multivariate regression? As stated heredummy variable trap needs to be avoided one category of each categorical feature removed after encoding but before training on input of algorithms that consider all the predictors together, as a linear combination.

Such algorithms are:. If you remove a category from input of a neural network that employs weight decay, it will get biased in favor of the omitted category instead. Even though no information is lost when omitting one category after encoding a feature, other algorithms will have to infer the correlation of the omitted category indirectly through combination of all the other categories, making them do more computation for the same result.

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Active 2 years, 4 months ago. Viewed 2k times. Ex: If I have categorical feature "size": "small", "medium", "large", then in one hot encoded I would have something like: small medium large other-feature 0 1 0 So to avoid dummy variable trap I need to remove any of the 3 columns, for example, column "small".

Active Oldest Votes. Robert Peetsalu Robert Peetsalu 76 4 4 bronze badges.In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Introductory neural network concerns are covered.

### Dummy Variable Trap in Regression Models

I didn't really know what I was doing at the time, and so things didn't go so well. As I have been spending a lot of time with Keras recently, I thought I would take another stab at this dataset in order to demonstrate building a simple neural network with Keras. The dataset, available hereis a collection of possible tac-tac-toe end-of-game board configurations, with 9 variables representing the 9 squares of a tic-tac-toe board, and a tenth class variable which designates if the described board configuration is a winning positive or not negative ending configuration for player X.

In short, does a particular collection of Xs and Os on a board mean a win for X? Incidentally, there arepossible ways of playing a game of tic-tac-toe. As is visible, each square can be designated as marked with an X, an O, or left blank b at game's end.

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The mapping of variables to physical squares is shown in Figure 1. Remember, the outcomes are positive or negative based on X winning. Figure 2 portrays a example board endgame layout, followed by its dataset representation. Figure 1. Mapping of variables to physical tic-tac-toe board locations. Figure 2. Example board endgame layout. In order to use the dataset to construct a neural network, some data preparation and transformations will be necessary. These are outlined below, as well as further commented upon in the code further down.

Next, we need to construct the neural network, which we will do using Keras. Let's keep in mind that our processed dataset has 18 variables to use as input, and that we are making binary class predictions as output. Figure 3. Visualization of network layers. The maximum accuracy of the trained network reached On the unseen test set, the neural network correctly predicted the class of of the instances.

This is not necessarily a true metric improvement, however.

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An independent verification set could possibly shed light on this By using different optimizers as opposed to Adama well as changing the number of hidden layers and the number of neurons per hidden layer, the resulting trained networks did not result in better loss, accuracy, or correctly classified test instances beyond what is reported above.

I hope this has been a useful introduction to constructing neural networks with Keras. The next such tutorial will attack convolutional neural network construction. By subscribing you accept KDnuggets Privacy Policy.

Subscribe to KDnuggets News. By Matthew MayoKDnuggets. Previous post. A data science journey, or wh Sign Up.In the context of a coding exercise inI was asked to write a sklearn pipeline and a tensorflow estimator for a dataset that describes employees and their wages. The goal: Create a predictor to predict if someone earns more or less than 50k a year. One of the issues I had was the handling of categorical values.

Of course, we all learned One-Hot-Encoding is a way to map this kind of data into a NN passable format. But I was asked to develop an exhaustive list of many ways of handling a categorical column. The column with the highest number of categories is the native-country with 41 unique occurring values. This list may hold thousands of unique values and these values are very difficult to handle by a neural network.

A good tool would encode the meaning of the categories in some meaningful way while keeping the number of dimensions relatively low. It turns out there are a number of ways to approach this problem. In the following article I will therefore create an overview of many ways to handle categorical data with neural networks. I will split the list into two categories: Generic approaches that can map any column of arbitrary symbols into appropriate representations and domain specific ones.

For the domain specific example I will describe ways of representing the country column in some meaningful way. Before diving into ways of handling categorical data and passing it to a neural network, I want to loose a few words to describe what it is.

In my own words, categorical data is a set of symbols that describe a certain higher level attribute of some object, system or entity of interest. How would you order the colors? How would you put them into relation with each other?

What is belongs to what? Which of the following four words would you assign a lower distance between each other? Often however, categorical data may be distinct or even overlapping.

Sometimes possible values of a category set can have relations between each other, sometimes they have nothing to do with each other whatsoever. All of these concepts need to be kept in mind when transferring categorical input into a numeric vector space.

In the words of Wikipedia:. Okay enough taking credit for other peoples work.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.

### Dummy Variable Trap in Regression Models

It only takes a minute to sign up. There are two different ways to encoding categorical variables. Say, one categorical variable has n values. One-hot encoding converts it into n variables, while dummy encoding converts it into n-1 variables.

If we have k categorical variables, each of which has n values. One hot encoding ends up with kn variables, while dummy encoding ends up with kn-k variables. I hear that for one-hot encoding, intercept can lead to collinearity problem, which makes the model not sound.

Someone call it " dummy variable trap ". Scikit-learn's linear regression model allows users to disable intercept. I do not see any "warning" on the website. Since one-hot encoding generates more variables, does it have more degree of freedom than dummy encoding? For an unregularized linear model with one-hot encoding, yes, you need to set the intercept to be false or else incur perfect collinearity.

For dummy encoding you should include an intercept, unless you have standardized all your variables, in which case the intercept is zero. The intercept is an additional degree of freedom, so in a well specified model it all equals out. For the second one, what if there are k categorical variables?

Is the degree of freedom still the same? You could not fit a model in which you used all the levels of both categorical variables, intercept or not. Say, I have 3 categorical variables, each of which has 4 levels. Am I correct?

The second thing does not actually work. You need to remove three columns, one from each of three distinct categorical encodings, to recover non-singularity of your design. We can examine what the design matrix would look like with and without an intercept by using model. We can see that when we use an intercept, model. So there is a total of 10 degrees of freedom. When we don't use an intercept, model.

So the number of degrees of freedom is still Sign up to join this community.Last Updated on May 20, Getting started in applied machine learning can be difficult, especially when working with real-world data. Often, machine learning tutorials will recommend or require that you prepare your data in specific ways before fitting a machine learning model.

In this post, you will discover the answer to these important questions and better understand data preparation in general in applied machine learning. Photo by Karan Jainsome rights reserved. Categorical variables are often called nominal. This type of categorical variable is called an ordinal variable. For example, a decision tree can be learned directly from categorical data with no data transform required this depends on the specific implementation.

Many machine learning algorithms cannot operate on label data directly. They require all input variables and output variables to be numeric. In general, this is mostly a constraint of the efficient implementation of machine learning algorithms rather than hard limitations on the algorithms themselves. This means that categorical data must be converted to a numerical form.

If the categorical variable is an output variable, you may also want to convert predictions by the model back into a categorical form in order to present them or use them in some application. The integer values have a natural ordered relationship between each other and machine learning algorithms may be able to understand and harness this relationship. For categorical variables where no such ordinal relationship exists, the integer encoding is not enough.

In fact, using this encoding and allowing the model to assume a natural ordering between categories may result in poor performance or unexpected results predictions halfway between categories. In this case, a one-hot encoding can be applied to the integer representation.

This is where the integer encoded variable is removed and a new binary variable is added for each unique integer value. In this post, you discovered why categorical data often must be encoded when working with machine learning algorithms. Do you have any questions? Post your questions to comments below and I will do my best to answer.

Thanks so much for the great, straightforward tutorial. I am trying to use the scikit-learn methods to determine how to convert my categorical data and I have a couple of questions.

Also once you have converted the categorical variable you have several new binary columns but you also still have the original text form of the categorical variable. Should I drop that text version of the categorical variable or just leave it there in the dataset? What are the arguments against? Also, the resulting one-hot encoded vectors are linearly independent rendering PCA ineffective quite a complex algorithm it is. HI Jason, I love your articles. Also, if there are many categorical variables, do we need to check multi-collinearity after one hot encoding e.

I also recommend testing an embedding, they work well for categorical features, especially high cardinality with interactions. HHi jason. I truly following you alot and really appreciate your effort and ease of tutorials.