15 Types of Regression you should know

Table of Contents

1. What is Regression Analysis?
2. Terminologies related to Regression
3. Types of Regressions

• Linear Regression
• Polynomial Regression
• Logistic Regression
• Quantile Regression
• Ridge Regression
• Lasso Regression
• ElasticNet Regression
• Principal Component Regression
• Partial Least Square Regression
• Support Vector Regression
• Ordinal Regression
• Poisson Regression
• Negative Binomial Regression
• Quasi-Poisson Regression
• Cox Regression

How to choose the correct Regression Model?

Regression Analysis Simplified

What is Regression Analysis?

Lets take a simple example : Suppose your manager asked you to predict annual sales. There can be a hundred of factors (drivers) that affects sales. In this case, sales is your dependent variable. Factors affecting sales are independent variables. Regression analysis would help you to solve this problem.

In simple words, regression analysis is used to model the relationship between a dependent variable and one or more independent variables.

It helps us to answer the following questions -

1. Which of the drivers have a significant impact on sales. 
2. Which is the most important driver of sales
3. How do the drivers interact with each other
4. What would be the annual sales next year.

Terminologies related to regression analysis

1. Outliers
Suppose there is an observation in the dataset which is having a very high or very low value as compared to the other observations in the data, i.e. it does not belong to the population, such an observation is called an outlier. In simple words, it is extreme value. An outlier is a problem because many times it hampers the results we get.

2. Multicollinearity
When the independent variables are highly correlated to each other then the variables are said to be multicollinear. Many types of regression techniques assumes multicollinearity should not be present in the dataset. It is because it causes problems in ranking variables based on its importance. Or it makes job difficult in selecting the most important independent variable (factor).

3. Heteroscedasticity
When dependent variable's variability is not equal across values of an independent variable, it is called heteroscedasticity. Example - As one's income increases, the variability of food consumption will increase. A poorer person will spend a rather constant amount by always eating inexpensive food; a wealthier person may occasionally buy inexpensive food and at other times eat expensive meals. Those with higher incomes display a greater variability of food consumption.

4. Underfitting and Overfitting
When we use unnecessary explanatory variables it might lead to overfitting. Overfitting means that our algorithm works well on the training set but is unable to perform better on the test sets. It is also known as problem of high variance.

When our algorithm works so poorly that it is unable to fit even training set well then it is said to underfit the data. It is also known as problem of high bias.

In the following diagram we can see that fitting a linear regression (straight line in fig 1) would underfit the data i.e. it will lead to large errors even in the training set. Using a polynomial fit in fig 2 is balanced i.e. such a fit can work on the training and test sets well, while in fig 3 the fit will lead to low errors in training set but it will not work well on the test set.

Regression : Underfitting and Overfitting

Types of Regression

Every regression technique has some assumptions attached to it which we need to meet before running analysis. These techniques differ in terms of type of dependent and independent variables and distribution.

Regression Analysis

When you have only 1 independent variable and 1 dependent variable, it is called simple linear regression.
When you have more than 1 independent variable and 1 dependent variable, it is called Multiple linear regression.

Multiple Regression Equation
Here 'y' is the dependent variable to be estimated, and X are the independent variables and ε is the error term. βi’s are the regression coefficients.

Assumptions of linear regression: 

1. There must be a linear relation between independent and dependent variables. 
2. There should not be any outliers present. 
3. No heteroscedasticity 
4. Sample observations should be independent. 
5. Error terms should be normally distributed with mean 0 and constant variance. 
6. Absence of multicollinearity and auto-correlation.

Interpretation of regression coefficients
Let us consider an example where the dependent variable is marks obtained by a student and explanatory variables are number of hours studied and no. of classes attended. Suppose on fitting linear regression we got the linear regression as:

Marks obtained = 5 + 2 (no. of hours studied) + 0.5(no. of classes attended)

> lm_coeff

     (Intercept)      Agriculture      Examination        Education         Catholic 
      66.9151817       -0.1721140       -0.2580082       -0.8709401        0.1041153 
Infant.Mortality 
       1.0770481 
> summary(model)

Call:
lm(formula = Fertility ~ ., data = swiss)

Residuals:
     Min       1Q   Median       3Q      Max 
-15.2743  -5.2617   0.5032   4.1198  15.3213 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)      66.91518   10.70604   6.250 1.91e-07 ***
Agriculture      -0.17211    0.07030  -2.448  0.01873 *  
Examination      -0.25801    0.25388  -1.016  0.31546    
Education        -0.87094    0.18303  -4.758 2.43e-05 ***
Catholic          0.10412    0.03526   2.953  0.00519 ** 
Infant.Mortality  1.07705    0.38172   2.822  0.00734 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.165 on 41 degrees of freedom
Multiple R-squared:  0.7067, Adjusted R-squared:  0.671 
F-statistic: 19.76 on 5 and 41 DF,  p-value: 5.594e-10

2. Polynomial Regression

In case of multiple variables say X1 and X2, we can create a third new feature (say X3) which is the product of X1 and X2 i.e.
In order to compare the results of linear and polynomial regression, firstly we fit linear regression:

model1 = lm(y ~x)
model1$fit
model1$coeff

> model1$fit
       1        2        3        4        5        6        7        8        9       10 
169.0995 178.9081 188.7167 218.1424 223.0467 266.6949 291.7068 296.6111 316.2282 335.8454 
> model1$coeff
 (Intercept)            x 
120.05663769   0.09808581 

new_x = cbind(x,x^2)

new_x

         x        
 [1,]  500  250000
 [2,]  600  360000
 [3,]  700  490000
 [4,] 1000 1000000
 [5,] 1050 1102500
 [6,] 1495 2235025
 [7,] 1750 3062500
 [8,] 1800 3240000
 [9,] 2000 4000000
[10,] 2200 4840000

Now we fit usual OLS to the new data:

model2 = lm(y~new_x)
model2$fit
model2$coeff

The fitted values and regression coefficients of polynomial regression are:

> model2$fit
       1        2        3        4        5        6        7        8        9       10 
122.5388 153.9997 182.6550 251.7872 260.8543 310.6514 314.1467 312.6928 299.8631 275.8110 
> model2$coeff
  (Intercept)        new_xx         new_x 
-7.684980e+01  4.689175e-01 -1.402805e-04 

Using ggplot2 package we try to create a plot to compare the curves by both linear and polynomial regression.

library(ggplot2)
ggplot(data = data) + geom_point(aes(x = Area,y = Price)) +
geom_line(aes(x = Area,y = model1$fit),color = "red") +
geom_line(aes(x = Area,y = model2$fit),color = "blue") +
theme(panel.background = element_blank())

3. Logistic Regression

Why don't we use linear regression in this case?

• The homoscedasticity assumption is violated.
• Errors are not normally distributed
• y follows binomial distribution and hence is not normal.

Examples

• HR Analytics: IT firms recruit large number of people, but one of the problems they encounter is after accepting the job offer many candidates do not join. So, this results in cost over-runs because they have to repeat the entire process again. Now when you get an application, can you actually predict whether that applicant is likely to join the organization (Binary Outcome - Join / Not Join).


• Elections: Suppose that we are interested in the factors that influence whether a political candidate wins an election. The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of money spent on the campaign and the amount of time spent campaigning negatively.

Predicting the category of dependent variable for a given vector X of independent variables

Through logistic regression we have 

Interpreting the logistic regression coefficients (Concept of Odds Ratio)


We fit logistic regression with glm( )  function and we set family = "binomial"

#Predicted Probablities

model$fitted.values

        1         2         3         4         5         6         7         8         9 
0.4545455 0.4545455 0.6428571 0.6428571 0.4545455 0.4545455 0.4545455 0.4545455 0.6428571 
       10        11        12        13        14        15        16        17        18 
0.6428571 0.4545455 0.4545455 0.6428571 0.6428571 0.6428571 0.4545455 0.6428571 0.6428571 
       19        20        21        22        23        24        25 
0.6428571 0.4545455 0.6428571 0.6428571 0.4545455 0.6428571 0.6428571 

data$prediction <- model$fitted.values>0.5

> data$prediction
 [1] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
[16] FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE

4. Quantile Regression

Basic Idea of Quantile Regression:In quantile regression we try to estimate the quantile of the dependent variable given the values of X's. Note that the dependent variable should be continuous.

The quantile regression model:
For qth quantile we have the following regression model:
This seems similar to linear regression model but here the objective function we consider to minimize is:
where q is the qth quantile.

If q  = 0.5 i.e. if we are interested in the median then it becomes median regression (or least absolute deviation regression) and substituting the value of q = 0.5 in above equation we get the objective function as:
Interpreting the coefficients in quantile regression:
Advantages of Quantile over Linear Regression

• Quite beneficial when heteroscedasticity is present in the data.
• Robust to outliers
• Distribution of dependent variable can be described via various quantiles.
• It is more useful than linear regression when the data is skewed.

Disclaimer on using quantile regression!
Quantile Regression in R

install.packages("quantreg")
library(quantreg)

model1 = rq(Fertility~.,data = swiss,tau = 0.25)
summary(model1)

tau: [1] 0.25

Coefficients:
                 coefficients lower bd upper bd
(Intercept)      76.63132      2.12518 93.99111
Agriculture      -0.18242     -0.44407  0.10603
Examination      -0.53411     -0.91580  0.63449
Education        -0.82689     -1.25865 -0.50734
Catholic          0.06116      0.00420  0.22848
Infant.Mortality  0.69341     -0.10562  2.36095

tau: [1] 0.5

Coefficients:
                 coefficients lower bd upper bd
(Intercept)      63.49087     38.04597 87.66320
Agriculture      -0.20222     -0.32091 -0.05780
Examination      -0.45678     -1.04305  0.34613
Education        -0.79138     -1.25182 -0.06436
Catholic          0.10385      0.01947  0.15534
Infant.Mortality  1.45550      0.87146  2.21101

model3 = rq(Fertility~.,data = swiss, tau = seq(0.05,0.95,by = 0.05))
quantplot = summary(model3)
quantplot

plot(quantplot)

We get the following plot:

Various quantiles are depicted by X axis. The red central line denotes the estimates of OLS coefficients and the dotted red lines are the confidence intervals around those OLS coefficients for various quantiles. The black dotted line are the quantile regression estimates and the gray area is the confidence interval for them for various quantiles. We can see that for all the variable both the regression estimated coincide for most of the quantiles. Hence our use of quantile regression is not justifiable for such quantiles. In other words we want that both the red and the gray lines should overlap as less as possible to justify our use of quantile regression.

5. Ridge Regression

1. Regularization

2. L1 Loss function or L1 Regularization

3. L2 Loss function or L2 Regularization

In general, L2 performs better than L1 regularization. L2 is efficient in terms of computation. There is one area where L1 is considered as a preferred option over L2. L1 has in-built feature selection for sparse feature spaces.  For example, you are predicting whether a person is having a brain tumor using more than 20,000 genetic markers (features). It is known that the vast majority of genes have little or no effect on the presence or severity of most diseases.

Very Important Note: 


On solving the above objective function we can get the estimates of β as:

How can we choose the regularization parameter λ?

If we choose lambda = 0 then we get back to the usual OLS estimates. If lambda is chosen to be very large then it will lead to underfitting. Thus it is highly important to determine a desirable value of lambda. To tackle this issue, we plot the parameter estimates against different values of lambda and select the minimum value of λ after which the parameters tend to stabilize.

X = swiss[,-1]
y = swiss[,1]

library(glmnet)

Using cv.glmnet( ) function we can do cross validation. By default alpha = 0 which means we are carrying out ridge regression. lambda is a sequence of various values of lambda which will be used for cross validation.

set.seed(123) #Setting the seed to get similar results.
model = cv.glmnet(as.matrix(X),y,alpha = 0,lambda = 10^seq(4,-1,-0.1))

best_lambda = model$lambda.min

ridge_coeff = predict(model,s = best_lambda,type = "coefficients")
ridge_coeff The coefficients obtained using ridge regression are:

6 x 1 sparse Matrix of class "dgCMatrix"
                           1
(Intercept)      64.92994664
Agriculture      -0.13619967
Examination      -0.31024840
Education        -0.75679979
Catholic          0.08978917
Infant.Mortality  1.09527837

6. Lasso Regression
We do not assume that the error terms are normally distributed.

Note that lasso regression also needs standardization.

Advantage of lasso over ridge regression

#Setting the seed for reproducibility
set.seed(123)
model = cv.glmnet(as.matrix(X),y,alpha = 1,lambda = 10^seq(4,-1,-0.1))
#By default standardize = TRUE

#Taking the best lambda
best_lambda = model$lambda.min
lasso_coeff = predict(model,s = best_lambda,type = "coefficients")
lasso_coeff The lasso coefficients we got are:

6 x 1 sparse Matrix of class "dgCMatrix"
                           1
(Intercept)      65.46374579
Agriculture      -0.14994107
Examination      -0.24310141
Education        -0.83632674
Catholic          0.09913931
Infant.Mortality  1.07238898

Which one is better - Ridge regression or Lasso regression?

Ridge regression is computationally more efficient over lasso regression. Any of them can perform better. So the best approach is to select that regression model which fits the test set data well.
7. Elastic Net Regression

It is a combination of both L1 and L2 regularization.
The objective function in case of Elastic Net Regression is:

R code for Elastic Net Regression

set.seed(123)
model = cv.glmnet(as.matrix(X),y,alpha = 0.5,lambda = 10^seq(4,-1,-0.1))
#Taking the best lambda
best_lambda = model$lambda.min
en_coeff = predict(model,s = best_lambda,type = "coefficients")
en_coeff

The coeffients we obtained are:

6 x 1 sparse Matrix of class "dgCMatrix"
                          1
(Intercept)      65.9826227
Agriculture      -0.1570948
Examination      -0.2581747
Education        -0.8400929
Catholic          0.0998702
Infant.Mortality  1.0775714

8. Principal Components Regression (PCR) 
PCR is a regression technique which is widely used when you have many independent variables OR multicollinearity exist in your data. It is divided into 2 steps:
The most common features of PCR are:

Getting the Principal components

Principal components analysis is a statistical method to extract new features when the original features are highly correlated. We create new features with the help of original features such that the new features are uncorrelated.

The first PC is having the maximum variance.
Similarly we can find the second PC U2 such that it is uncorrelated with U1 and has the second largest variance.
In a similar manner for 'p' features we can have a maximum of 'p' PCs such that all the PCs are uncorrelated with each other and the first PC has the maximum variance, then 2nd PC has the maximum variance and so on.

Drawbacks:

Principal Components Regression in R

View(data)  This is how some of the observations in our dataset will look like:
We use pls package in order to run PCR.

install.packages("pls")
library(pls)

pcr_model <- pcr(Employed~., data = data1, scale = TRUE, validation = "CV")
summary(pcr_model)

Data:  X dimension: 16 5 
 Y dimension: 16 1
Fit method: svdpc
Number of components considered: 5

VALIDATION: RMSEP
Cross-validated using 10 random segments.
       (Intercept)  1 comps  2 comps  3 comps  4 comps  5 comps
CV           3.627    1.194    1.118   0.5555   0.6514   0.5954
adjCV        3.627    1.186    1.111   0.5489   0.6381   0.5819

TRAINING: % variance explained
          1 comps  2 comps  3 comps  4 comps  5 comps
X           72.19    95.70    99.68    99.98   100.00
Employed    90.42    91.89    98.32    98.33    98.74

Here in the RMSEP the root mean square errors are being denoted. While in 'Training: %variance explained' the cumulative % of variance explained by principle components is being depicted. We can see that with 3 PCs more than 99% of variation can be attributed.
We can also create a plot depicting the mean squares error for the number of various PCs.

validationplot(pcr_model,val.type = "MSEP")

By writing val.type = "R2"we can plot the R square for various no. of PCs.

validationplot(pcr_model,val.type = "R2")

 If we want to fit pcr for 3 principal components and hence get the predicted values we can write:

pred = predict(pcr_model,data1,ncomp = 3)

9. Partial Least Squares (PLS) Regression 

It is an alternative technique of principal component regression when you have independent variables highly correlated. It is also useful when there are a large number of independent variables.

Difference between PLS and PCR

Both techniques create new independent variables called components which are linear combinations of the original predictor variables but PCR creates components to explain the observed variability in the predictor variables, without considering the response variable at all. While PLS takes the dependent variable into account, and therefore often leads to models that are able to fit the dependent variable with fewer components.

PLS Regression in R

library(plsdepot)
data(vehicles)
pls.model = plsreg1(vehicles[, c(1:12,14:16)], vehicles[, 13], comps = 3)
# R-Square
pls.model$R2

10. Support Vector Regression

Support vector regression can solve both linear and non-linear models. SVM uses non-linear kernel functions (such as polynomial) to find the optimal solution for non-linear models.

The main idea of SVR is to minimize error, individualizing the hyperplane which maximizes the margin.

library(e1071)
svr.model <- svm(Y ~ X , data)
pred <- predict(svr.model, data)
points(data$X, pred, col = "red", pch=4)

11. Ordinal Regression

Ordinal Regression is used to predict ranked values. In simple words, this type of regression is suitable when dependent variable is ordinal in nature. Example of ordinal variables - Survey responses (1 to 6 scale), patient reaction to drug dose (none, mild, severe).

Why we can't use linear regression when dealing with ordinal target variable?

In linear regression, the dependent variable assumes that changes in the level of the dependent variable are equivalent throughout the range of the variable. For example, the difference in weight between a person who is 100 kg and a person who is 120 kg is 20kg, which has the same meaning as the difference in weight between a person who is 150 kg and a person who is 170 kg. These relationships do not necessarily hold for ordinal variables.

library(ordinal)
o.model <- clm(rating ~ ., data = wine)
summary(o.model)