- Linear regression
- Logistic regression
- Linear and Logistic regression with regularization
- Neural network
- Support Vector Machine
- Naive Bayes
- Nearest Neighbor
- Decision Tree
- Random Forest
- Gradient Boosted Trees

For classification problem, we use the "iris" data set and predict its "species" from its "width" and "length" measures of sepals and petals. Here is how we setup our training and testing data.

```
> summary(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width
Min. :4.300000 Min. :2.000000 Min. :1.000 Min. :0.100000
1st Qu.:5.100000 1st Qu.:2.800000 1st Qu.:1.600 1st Qu.:0.300000
Median :5.800000 Median :3.000000 Median :4.350 Median :1.300000
Mean :5.843333 Mean :3.057333 Mean :3.758 Mean :1.199333
3rd Qu.:6.400000 3rd Qu.:3.300000 3rd Qu.:5.100 3rd Qu.:1.800000
Max. :7.900000 Max. :4.400000 Max. :6.900 Max. :2.500000
Species
setosa :50
versicolor:50
virginica :50
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
>
# Prepare training and testing data
> testidx <- which(1:length(iris[,1])%%5 == 0)
> iristrain <- iris[-testidx,]
> iristest <- iris[testidx,]
```

For regression problem, we use the "Prestige" data set (imported from the "car" R package) and predict the degree of "prestige" from a set of input variables such as "income", "education", "job types" ... etc. Here is how we setup our training and testing data.

```
> library(car)
> summary(Prestige)
education income women
Min. : 6.38000 Min. : 611.000 Min. : 0.00000
1st Qu.: 8.44500 1st Qu.: 4106.000 1st Qu.: 3.59250
Median :10.54000 Median : 5930.500 Median :13.60000
Mean :10.73804 Mean : 6797.902 Mean :28.97902
3rd Qu.:12.64750 3rd Qu.: 8187.250 3rd Qu.:52.20250
Max. :15.97000 Max. :25879.000 Max. :97.51000
prestige census type
Min. :14.80000 Min. :1113.000 bc :44
1st Qu.:35.22500 1st Qu.:3120.500 prof:31
Median :43.60000 Median :5135.000 wc :23
Mean :46.83333 Mean :5401.775 NA's: 4
3rd Qu.:59.27500 3rd Qu.:8312.500
Max. :87.20000 Max. :9517.000
> head(Prestige)
education income women prestige census type
gov.administrators 13.11 12351 11.16 68.8 1113 prof
general.managers 12.26 25879 4.02 69.1 1130 prof
accountants 12.77 9271 15.70 63.4 1171 prof
purchasing.officers 11.42 8865 9.11 56.8 1175 prof
chemists 14.62 8403 11.68 73.5 2111 prof
physicists 15.64 11030 5.13 77.6 2113 prof
> testidx <- which(1:nrow(Prestige)%%4==0)
> prestige_train <- Prestige[-testidx,]
> prestige_test <- Prestige[testidx,]
```

To measure the performance of our prediction, here we use some simple metrics to get through this basic ideas. For regression problem, we'll use the correlation between our prediction and the testing result. For classification problem, we'll use the contingency table to measure the count of true/false positive/negative in our prediction.

Now we have set the stage, lets get started.

## Linear Regression

Linear regression has the longest history in statistics, well understood and is the most popular machine learning model. It is based on the assumption of a linear relationship exist between the input x1, x2 ... and output variable y (numeric).y = Ɵ

_{0}+ Ɵ

_{1}x

_{1}+ Ɵ

_{2}x

_{2}+ …

The learning algorithm will learn the set of parameter such that the objective function: sum of square error ∑(y

_{actual}- y

_{estimate})

^{2}is minimized.

Here is the code examples in R.

```
> model <- lm(prestige~., data=prestige_train)
> summary(model)
Call:
lm(formula = prestige ~ ., data = prestige_train)
Residuals:
Min 1Q Median 3Q Max
-13.9078951 -5.0335742 0.3158978 5.3830764 17.8851752
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -20.7073113585 11.4213272697 -1.81304 0.0743733 .
education 4.2010288017 0.8290800388 5.06710 0.0000034862 ***
income 0.0011503739 0.0003510866 3.27661 0.0016769 **
women 0.0363017610 0.0400627159 0.90612 0.3681668
census 0.0018644881 0.0009913473 1.88076 0.0644172 .
typeprof 11.3129416488 7.3932217287 1.53018 0.1307520
typewc 1.9873305448 4.9579992452 0.40083 0.6898376
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 7.41604 on 66 degrees of freedom
(4 observations deleted due to missingness)
Multiple R-squared: 0.820444, Adjusted R-squared: 0.8041207
F-statistic: 50.26222 on 6 and 66 DF, p-value: < 0.00000000000000022204
> # Use the model to predict the output of test data
> prediction <- predict(model, newdata=prestige_test)
> # Check for the correlation with actual result
> cor(prediction, prestige_test$prestige)
[1] 0.9376719009
```

The coefficient column gives an estimation of Ɵi, there is an associated p-value that gives the confidence of each estimated Ɵi. For example, features not marked with at least one * can be safely ignored.

In the above model, education and income has a high influence to the prestige.

Linear regression has certain assumption about the underlying distribution of data which we need to validate. This include the residuals (error) has normal distribution with a zero mean and constant variation.

```
> # verify if the residuals are normally distributed
> rs <- residuals(model)
> qqnorm(rs)
> qqline(rs)
> shapiro.test(rs)
Shapiro-Wilk normality test
data: rs
W = 0.9744, p-value = 0.1424
```

Here is the plot of how the residual aligned with the normal distribution.

## Logistic Regression

Logistic Regression is basically applying a transformation of the output of a linear regression such that it fits into the value of 0 to 1, and hence mimic the probability of a binary output. Logistic regression is the most popular machine learning technique applied in solving classification problem.In a classification problem, the output is binary rather than numeric, we can imagine of doing a linear regression and then compress the numeric output into a 0..1 range using the logit function

1/(1+e

^{-t})

y = 1/(1 + e

^{-(Ɵ}_{0}^{ + Ɵ}_{1}^{x}_{1}^{ + Ɵ}_{2}^{x}_{2}^{ + …)})The objective function in logistic regression is different. It is minimizing the entropy as follows ...

∑ (y

_{actual}* log y

_{estimate}+ (1 - y

_{actual}) * log(1 - y

_{estimate}))

Here is the example R code to classify iris data.

```
> newcol = data.frame(isSetosa=(iristrain$Species == 'setosa'))
> traindata <- cbind(iristrain, newcol)
> head(traindata)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species isSetosa
1 5.1 3.5 1.4 0.2 setosa TRUE
2 4.9 3.0 1.4 0.2 setosa TRUE
3 4.7 3.2 1.3 0.2 setosa TRUE
4 4.6 3.1 1.5 0.2 setosa TRUE
6 5.4 3.9 1.7 0.4 setosa TRUE
7 4.6 3.4 1.4 0.3 setosa TRUE
> formula <- isSetosa ~ Sepal.Length + Sepal.Width
+ Petal.Length + Petal.Width
> logisticModel <- glm(formula, data=traindata,
family="binomial")
> # Predict the probability for test data
> prob <- predict(logisticModel, newdata=iristest, type='response')
> round(prob, 3)
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75
1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
80 85 90 95 100 105 110 115 120 125 130 135 140 145 150
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
```

## Regression with Regularization

Notice our assumption of linearity between input and output variable above is not necessary true. One technique is to combine existing features by multiplying them together and hope we are lucky enough to hit some useful combination. Therefore we may end up having a large set of input features in our machine learning.When we have a large size of input variable but moderate size of training data, we are subjected to the overfitting problem, which is our model fits too specific to the training data and not generalized enough for the data we haven't seen. Regularization is the technique of preventing the model to fit too specifically to the training data.

In linear regression, it is found that overfitting happens when Ɵ has a large value. So we can add a penalty that is proportional to the magnitude of Ɵ. In L2 regularization (also known as Ridge regression), Σ square(Ɵi) will be added to the cost function, while In L1 regularization (also known as Lasso regression), Σ ||Ɵi|| will be added to the cost function.

Both L1, L2 will shrink the magnitude of Ɵi, L2 tends to make dependent input variables having the same coefficient while L tends to pick of the coefficient of variable to be non-zero and other zero. In other words, L1 regression will penalize the coefficient of redundant variables that are linearly dependent and is frequently used to remove redundant features.

Combining L1 and L2, the general form of cost function becomes

Cost == Non-regularization-cost + λ (α.Σ ||Ɵi|| + (1- α).Σ square(Ɵi))

Notice there are 2 tunable parameters, lambda λ and alpha α. Lambda controls the degree of regularization (0 means no-regularization, infinity means ignoring all input variables because all coefficients of them will be zero). Alpha controls the degree of mix between L1 and L2. (0 means pure L2 and 1 means pure L1).

Glmnet is a popular regularization package. The alpha parameter need to be supplied based on the application need (for selecting a reduced set of variables, alpha=1 is preferred). The library provides a cross-validation test to automatically figure out what is the better lambda value.

Lets repeat the linear regression "prestige" data set and use regularization this time, we pick alpha = 0.7 to favor L1 regularization.

```
> library(glmnet)
> cv.fit <- cv.glmnet(as.matrix(prestige_train[,c(-4, -6)]),
as.vector(prestige_train[,4]),
nlambda=100, alpha=0.7,
family="gaussian")
> plot(cv.fit)
> coef(cv.fit)
5 x 1 sparse Matrix of class "dgCMatrix"
1
(Intercept) 6.3876684930151
education 3.2111461944976
income 0.0009473793366
women 0.0000000000000
census 0.0000000000000
> prediction <- predict(cv.fit,
newx=as.matrix(prestige_test[,c(-4, -6)]))
> cor(prediction, as.vector(prestige_test[,4]))
[,1]
1 0.9291181193
```

Here is the cross-validation plot, which shows the best lambda with minimal mean square error.

Also we can repeat the logistic regression "iris" data set and use regularization this time, we pick alpha = 0.8 to even favor L1 regularization more.

```
> library(glmnet)
> cv.fit <- cv.glmnet(as.matrix(iristrain[,-5]),
iristrain[,5], alpha=0.8,
family="multinomial")
> prediction <- predict(cv.fit,
newx=as.matrix(iristest[,-5]),
type="class")
> table(prediction, iristest$Species)
prediction setosa versicolor virginica
setosa 10 0 0
versicolor 0 10 2
virginica 0 0 8
```

Instead of picking lambda (the degree of regularization) based on cross validation, we can also based on the number of input variables that we want to retain. So we can plot the "regularization path" which shows how the coefficient of each input variables changes when the lambda changes and pick the right lambda that filter out the number of input variables for us.

```
> # try alpha = 0, Ridge regression
> fit <- glmnet(as.matrix(iristrain[,-5]),
iristrain[,5], alpha=0,
family="multinomial")
> plot(fit)
> # try alpha = 0.8, Elastic net
> fit <- glmnet(as.matrix(iristrain[,-5]),
iristrain[,5], alpha=0.8,
family="multinomial")
> plot(fit)
> # try alpha = 1, Lasso regression
> fit <- glmnet(as.matrix(iristrain[,-5]),
iristrain[,5], alpha=1,
family="multinomial")
> plot(fit)
```

Here is the output of these plots. Notice that as Alpha is closer to 1 (L1 regularization), it tends to have a stronger filter to the number of input variables.

In my next post, I will cover the other machine learning techniques.