There are several ways by which you can overcome class imbalances problem in a predictive model.

Increase the case weights for the samples in the minority classes.

1. Adjusting Prior Probabilities

Prior probability is the proportion of events and non-events in the imbalance classes. Using more balanced priors or a balanced training set may help deal with a class imbalance.

**In CART, you can specify prior probability assigned to each class to adjust the importance of misclassifications for each class.**

library(rpart)

library(rattle)

data(audit)

audit.rpart <- rpart(Adjusted ~ .,data=audit[,-12],parms=list(prior=c(.5,.5)))

**2. Cost Sensitive Training / Loss Learning**

It employs the misclassification costs into the learning algorithm. In a marketing predictive model, a false positive costs just one extra direct mail while a true positive may lead to conversion worth $100. In a healthcare model, a false negative screening for tuberculosis could be destructive.

In cost-sensitive training, we assign no cost to correct classifications (Cost(TP) and Cost(TN). In other words, cost(TP) and cost(TN) are set to0. We assignhigher costto FALSE NEGATIVE (actual event predicted as non-event) than FALSE POSITIVE as prediction to event is our objective.

**Total Cost =**(FN × CFN) + (FP × CFP)library(rpart)

library(rattle)

data(audit)

loss <- matrix(c(0, 1, 20, 0), ncol=2)

audit.rpart <- rpart(Adjusted ~ ., data=audit[,-12], parms=list(loss=loss))

The cost of mis-classifying a positive example as a negative observation (FN) as 20 units and cost of mis-classifying a negative example as positive (FP) as 1 unit.

**C5.0 algorithm**has similar syntax to rpart by taking a cost matrix, although this function uses

**the transpose of the cost matrix structure used by rpart**:

library(C50)

library(rattle)

data(audit)

loss <- matrix(c(0, 20, 1, 0), ncol=2)

audit.rpart <- C50(Adjusted ~ ., data=audit[,-12], parms=list(loss=loss))

library(rattle)

data(audit)

loss <- matrix(c(0, 20, 1, 0), ncol=2)

audit.rpart <- C50(Adjusted ~ ., data=audit[,-12], parms=list(loss=loss))

**3. Sampling**

You can perform oversampling of events i.e. reducing non-events so that ratio gets rougly equal or classes become less skewed.

**4. Assigning large case weights to events**

**It applies to each observation**.**Weights based on the size of the risk**library(rpart)

library(rattle)

data(audit)

audit$weight <- abs(audit$RISK_Adjustment)/max(audit$RISK_Adjustment)*10+1

audit.rpart <- rpart(TARGET_Adjusted ~ ., data=audit[,-12], weights=weight)

Case Weights : Rare Event Model |

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