The biggest strength but also the biggest weakness of the linear regression model is that the prediction is modeled as a weighted sum of the features. In addition, the linear model comes with many other assumptions. The bad news is well, not really news that all those assumptions are often violated in reality: The outcome given the features might have a non-Gaussian distribution, the features might interact and the relationship between the features and the outcome might be nonlinear.

The good news is that the statistics community has developed a variety of modifications that transform the linear regression model from a simple blade into a Swiss knife. This chapter is definitely not your definite guide to extending linear models.

After reading, you should have a solid overview of how to extend linear models. If you want to learn more about the linear regression model first, I suggest you read the chapter on linear regression modelsif you have not already. By forcing the data into this corset of a formula, we obtain a lot of model interpretability. The linear model allows us to compress the relationship between a feature and the expected outcome into a single number, namely the estimated weight. But a simple weighted sum is too restrictive for many real world prediction problems.

In this chapter we will learn about three problems of the classical linear regression model and how to solve them. There are many more problems with possibly violated assumptions, but we will focus on the three shown in the following figure:. Reality usually does not adhere to those assumptions right side : Outcomes might have non-Gaussian distributions, features might interact and the relationship might be nonlinear. Problem : The target outcome y given the features does not follow a Gaussian distribution.

Example : Suppose I want to predict how many minutes I will ride my bike on a given day. As features I have the type of day, the weather and so on.

If I use a linear model, it could predict negative minutes because it assumes a Gaussian distribution which does not stop at 0 minutes. Also if I want to predict probabilities with a linear model, I can get probabilities that are negative or greater than 1.

Problem : The features interact. Example : On average, light rain has a slight negative effect on my desire to go cycling. But in summer, during rush hour, I welcome rain, because then all the fair-weather cyclists stay at home and I have the bicycle paths for myself!

This is an interaction between time and weather that cannot be captured by a purely additive model. Solution : Adding interactions manually. Problem : The true relationship between the features and y is not linear.

Example : Between 0 and 25 degrees Celsius, the influence of the temperature on my desire to ride a bike could be linear, which means that an increase from 0 to 1 degree causes the same increase in cycling desire as an increase from 20 to But at higher temperatures my motivation to cycle levels off and even decreases - I do not like to bike when it is too hot.

The solutions to these three problems are presented in this chapter. Many further extensions of the linear model are omitted. If I attempted to cover everything here, the chapter would quickly turn into a book within a book about a topic that is already covered in many other books.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.

It only takes a minute to sign up. I thought that generalized linear model GLM would be considered a statistical model, but a friend told me that some papers classify it as a machine learning technique. Which one is true or more precise?

Any explanation would be appreciated. A GLM is absolutely a statistical model, but statistical models and machine learning techniques are not mutually exclusive. In general, statistics is more concerned with inferring parameters, whereas in machine learning, prediction is the ultimate goal.

Regarding prediction, statistics and machine learning sciences started to solve mostly the same problem from different perspectives. Basically statistics assumes that the data were produced by a given stochastic model. So, from a statistical perspective, a model is assumed and given various assumptions the errors are treated and the model parameters and other questions are inferred. Machine learning comes from a computer science perspective.

The models are algorithmic and usually very few assumptions are required regarding the data. We work with hypothesis space and learning bias. The best exposition of machine learning I found is contained in Tom Mitchell's book called Machine Learning. For a more exhaustive and complete idea regarding the two cultures you can read the Leo Breiman paper called Statistical Modeling: The Two Cultures.

However what must be added is that even if the two sciences started with different perspectives, both of them now now share a fair amount of common knowledge and techniques. Why, because the problems were the same, but the tools were different. So now machine learning is mostly treated from a statistical perspective check the Hastie,Tibshirani, Friedman book The Elements of Statistical Learning from a machine learning point of view with a statistical treatement, and perhaps Kevin P. Murphy 's book Machine Learning: A probabilistic perspectiveto name just a few of the best books available today.

Even the history of the development of this field show the benefits of this merge of perspectives.

### Linear Models, Non-Linear Models & Feature Transformations

I will describe two events. The first is the creation of CART trees, which was created by Breiman with a solid statistical background. At approximately the same time, Quinlan developed ID3,C45,See5, and so on, decision tree suite with a more computer science background. Now both this families of trees and the ensemble methods like bagging and forests become quite similar. The second story is about boosting. Initially they were developed by Freund and Shapire when they discovered AdaBoost.Generalized linear models GLM are a framework for a wide range of analyses.

They relax the assumptions for a standard linear model in two ways. Second, you can specify a distribution for the response variable. The rxGlm function in RevoScaleR provides the ability to estimate generalized linear models on large data sets. Any valid R family object that can be used with glm can be used with rxGlmincluding user-defined.

The Poisson family is used to estimate models of count data. Examples from the literature include the following types of response variables:. The data are from a placebo-controlled clinical trial of 59 epileptics. Patients with partial seizures were enrolled in a randomized clinical trial of the anti-epileptic drug, progabide.

Counts of epileptic seizures were recorded during the trial. The data set also includes a baseline 8-week seizure count and the age of the patient.

To access this data, first make sure the robust package is installed, then use the data command to load the data frame:. The data set has 59 observations, and 12 variables. The variables of interest are BaseAgeTrtand sumY. To estimate a model with sumY as the response variable and the Base number of seizures, Ageand the treatment as explanatory variables, we can use rxGlm.

A benefit to using rxGlm is that the code scales for use with a much bigger data set. To interpret the coefficients, it is sometimes useful to transform them back to the original scale of the dependent variable. In this case:. A common method of checking for overdispersion is to calculate the ratio of the residual deviance with the degrees of freedom.

This should be about 1 to fit the assumptions of the model. The quasi-poisson family can be used to handle over-dispersion. In this case, instead of assuming that the variance and mean are one, a relationship is estimated from the data:.

Notice that the coefficients are the same as when using the poisson family, but that the standard errors are larger. The effect of the treatment is no longer significant. The Gamma family is used with data containing positive values with a positive skew. A classic example is estimating the value of auto insurance claims. Using the sample claims. The Tweedie family of distributions provides flexible models for estimation.

The power parameter var. If var. We consider the annual cost of property insurance for heads of household ages 21 through 89, and its relationship to age, sex, and region. First, to create the subsample specify the correct data path for your downloaded data :. The blocksPerRead argument is ignored when run locally using R Client. Learn more An Xdf data source representing the new data file is returned. The new data file has over 5 million observations. The variable region has some long factor level character strings, and it also has a number of levels for which there are no observations. We can see this using rxSummary :.In this article, we aim to discuss various GLMs that are widely used in the industry. We focus on: a log-linear regression b interpreting log-transformations and c binary logistic regression. Generalized Linear Model GLM helps represent the dependent variable as a linear combination of independent variables. Simple linear regression is the traditional form of GLM.

Simple linear regression works well when the dependent variable is normally distributed. The assumption of normally distributed dependent variable is often violated in real situations.

For example, consider a case where dependent variable can take only positive values and has fat tail. The dependent variable is number of coffee sold and the independent variable is the temperature.

Let's assume that we have modeled a linear relationship between the variables. The expected number of coffee sold decreases by 10 units as temperature increases by 1 degree. The problem with this kind of model is that it can give meaningless results.

There will be situation when a increase of 1 degree in temperature would force the model to output negative number for number of coffee sold. GLM comes in handy in these types of situations. GLM is widely used to model situations where the independent variable has arbitrary distributions i. The basic intuition behind GLM is to not model dependent variable as a linear combination of independent variable but model a function of dependent variable as a linear combination of dependent variable.

This function used to transform independent variable is known as link function. In the above example the distribution of number of coffee sold will not be normal but poisson and the log transformation log will be the link function in this case of the variable before regression would lead to a logical model.

The ability of GLM to transform data with arbitrary distribution to fit a meaningful linear model makes it a powerful tool. We also review the underlying distributions and the applicable link functions. However, we start the article with a brief discussion on the traditional form of GLM, simple linear regression. Along with the detailed explanation of the above model, we provide the steps and the commented R script to implement the modeling technique on R statistical software.Prediction and causal explanation are fundamentally distinct tasks of data analysis. Nevertheless, these two concepts are often conflated in practice. We use the framework of generalized linear models GLMs to illustrate that predictive and causal queries require distinct processes for their application and subsequent interpretation of results.

In particular, we identify five primary ways in which GLMs for prediction differ from GLMs for causal inference: i the covariates that should be considered for inclusion in and possibly exclusion from the model; ii how a suitable set of covariates to include in the model is determined; iii which covariates are ultimately selected and what functional form i.

We outline some of the potential consequences of failing to acknowledge and respect these differences, and additionally consider the implications for machine learning ML methods.

We then conclude with three recommendations that we hope will help ensure that both prediction and causal modelling are used appropriately and to greatest effect in health research. Key Messages The distinct goals of prediction and causal explanation result in distinct modelling processes, but this is underappreciated in current modelling applications in health research e.

Modelling methods that are optimized for prediction are not necessarily optimized for causal inference. Failure to recognise the distinction between modelling strategies for prediction and causal inference in machine learning applications risks wasting financial resources and creates confusion in both academic and public discourse. Although many of the same techniques e. This is perhaps most easily demonstrated in the context of generalized linear models GLMsbut has applicability to other modelling methodologies, including machine learning ML.

For this reason, we attempt here to simply and concisely illustrate the key differences between prediction and causal inference in the context of GLMs, to outline the potential consequences of failing to acknowledge and respect these differences, and to provide recommendations that might enable prediction and causal modelling to be used effectively in health research. The emergence of programmable desktop computers in the s and s therefore facilitated a revolution in data analytics, since it became possible to perform both swiftly and automatically the complex matrix inversions required for generalized linear modelling.

However, the routine application of generalized linear modelling that became established and entrenched was unwittingly predicated on prediction, rather than causal explanation. Standard GLMs are agnostic to the causal structure of the data to which they are fitted. The process of fitting a GLM makes no assumptions about causality, nor does it enable any conclusions about causality to be drawn without further strong assumptions.

This may be done explicitly or implicitly, as in a recent though by no means unique high-profile study that found a significant association between active commuting and lower risk of cardiovascular disease but then used this as the basis for recommending initiatives that support active commuting.

These factors have combined to produce ambiguity about how GLMs for prediction differ from GLMs for causal inference, often resulting in the conflation of two distinct concepts. Models for prediction are concerned with optimally deriving the likely value or risk of an outcome [i. In contrast, models for causal inference are concerned with optimally deriving the likely change in an outcome [i.

Models for prediction and causal inference are thus fundamentally distinct in terms of their purpose and utility, and methods optimized for one cannot be assumed to be optimal for the other. GLMs for prediction and causal inference differ with respect to the following. The covariates that should be considered for inclusion in and possibly exclusion from the model. Which covariates are ultimately selected, and what functional form i. To illustrate these differences, we use for context a recent study by Pabinger et al.

We consider how two research questions—one predictive, one causal—might be addressed using logistic regression i.It has been long time since I wrote the first machine learning for everyone article. From now on, I will try to publish articles more frequently. Quick Note: Unfortunately, Medium does not support mathematical type setting Latex etc.

The goal of linear regression models is to find a linear mapping between observed features and observed real outputs so that when we see a new instance, we can predict the output. In this article, we accepted that there are N observations with output y and M features xfor training. We define an M dimensional vector w to represent weights which map inputs to outputs.

We also define N by M dimensional matrix X to represent all the inputs. Our aim is to find best w that minimizes the Eucledian distance between real output vector y and approximation X w.

For this purpose, we generally use Least Squares error and matrix calculus to minimize it. Here we use L to represent loss error function. This is a linear algebraic approximation to problem, but in order to understand the problem better, and extend it to different problem settings, we will handle it in a more probabilistic manner. In the begginning, we said that outputs are real.

Actually, we assumed that outputs are sampled from Normal distribution. Now, our aim is to find w that maximizes the likelihood of y which is p y X, w.

We defined p y X, w as Normal distribution above, so we know its expanded form which is pdf of Normal distribution. It is hard to work directly with likelihood function, instead, we will work with loglikelihood which has the same maxima and minima with likelihood. We can either maximize loglikelihood or minimize negative loglikelihood. We choose the second one and call it loss function.

This loss function is exactly same with the least squares error function. So we statistically explained linear regression and this will be very helpful in upcoming models. The solution above is called maximum likelihood method because that is what we exactly did, maximizing likelihood.

## Generalized Linear Models using RevoScaleR

We can put prior probabilities on weights and maximize posterior distribution of w instead of likelihood of y. In above equations, we defined zero mean, unit variance prior on weight wand derived loss function by using negative log posterior distribution.

16. Learning: Support Vector Machines

Prior distribution of w try to keep weight values around its mean which is 0 in this case.We use cookies to give you a better experience. This means it is no longer being updated or maintained, so information within the course may no longer be accurate.

FutureLearn accepts no liability for any loss or damage arising as a result of use or reliance on this information. A linear model is one that outputs a weighted sum of the inputs, plus a bias intercept term. Where there is a single input feature, X, and a single target variable, Y, this is of the form:.

It is clear how such a model functions as a regression model - the output is simply the estimate of the value for the target variable and the hyperplane is the regression curve.

They can also be binary classifiers. In such linear classifiers, the hyperplane given by the model specifies a decision boundary rather than a regression curve. Linear models have a number of advantages: They are easy to interpret, and fast to train and use, since the mathematics involved is simple to compute. Unfortunately, though, the real world is seldom linear. This means that linear models are normally too simple to be able to adequately model real world systems.

Instead, we often need to use non-linear models. Let us assume we have the data given below. We wish to generate a model that estimates the value of Y given X. However, instead of looking for a linear relationship between X and Y, we could look for a linear relationship between the transformations of X and Y.

This is entirely legitimate. By transformation we simply mean functions of X, and any function of a random variable or set of random variables is itself a random variable. Although we only have one input feature in this example, note that in the general case such each transformation function would be arbitrary functions of all input features.

We can now consider the relationship between our target variable and these latent variables. Using OLS to model this relationship we generate the following model:. This curve is non-linear, and if we pick it out:. We have thereby obtained a non-linear model in our original data by combining a linear method with non-linear transformation of our original data. This approach is one that is we will encounter repeatedly being used to turn both linear regression and linear classification models in much more flexible non-linear models. The key to understanding what is going on is that we are producing a linear model in a high dimensional space where the data coordinates are given by non-linear transforms of the original input features. This results in a linear surface in the higher dimensional space.

Similarly, we could proceed by looking for linear relationships between X and non-linear transformations of Y. In fact, such models are known as generalized linear models GLMs and in the related nomenculture the transformation of Y is known as the link function.

GLMs are used to model data with a wide range of common distribution types see here. Note that logistic regression, which we will see used as a linear classifier in combination with non-linear transformations, is just such a GLM.

We will make use of another GLM, Poisson regression, in some early video exercises. If you are unfamiliar with Poisson regression models you may like to review them. Enhance your skills in practical data mining as you get to grips with using large data sets and advanced data mining techniques.

Included in Unlimited. Learn how the games industry can use big data to enhance the gaming experience and increase profits. Search term Search. Want to keep learning? Join the course to learn more.

View course. Linear Models A linear model is one that outputs a weighted sum of the inputs, plus a bias intercept term.