$1.$

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Confusion matrix

Data science

Decision tree

Gini impurity

Overfitting

Predictive modelling

Pruning

Receiver operating characteristic $($ROC$)$ curve

refers to the use of data analytics to draw conclusions from data.

$$

Confusion matrix

Data science

Decision tree

Gini impurity

Overfitting

Predictive modelling

Pruning

Receiver operating characteristic $($ROC$)$ curve

is a data science technique used to make predictions based on past or current data.

Part $2$

$2.$ The

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confusion matrix

data science

decision tree

Gini impurity

overfitting

predictive modelling

pruning

receiver operating characteristic $($ROC$)$ curve

is a technique for segmenting the target variable into different regions based on a set of rules.

$$

Confusion matrix

Data science

Decision tree

Gini impurity

Overfitting

Predictive modelling

Pruning

Receiver operating characteristic $($ROC$)$ curve

is a way to measure the purity of a collection of observations in a rectangle$($set$).$ Two specific areas that management accountants address when refining a decision tree to better match the economics of business are

$$

the decision tree

Gini impurity

overfitting

predictive modelling

and

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the confusion matrix.

data science.

pruning.

the receiver operating characteristic $($ROC$)$ curve.

Part $3$

$3.$ A useful and commonly used tool to evaluate a model is the

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confusion matrix

data science

decision tree

Gini impurity

overfitting

predictive modelling

pruning

receiver operating characteristic $($ROC$)$ curve

that plots the false positive rate$($FP$)$ on the x$-$axis and the true positive rate$($TP$)$ on the y$-$axis.

Part $4$

$4.$ A

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confusion matrix

data science

decision tree

Gini impurity

overfitting

predictive modelling

pruning

receiver operating characteristic $($ROC$)$ curve

is a matrix that shows the predicted and actual classifications at a given threshold value.