Assume your data mining problem has two classes in it$.$

Review Figure $4\mathrm{.}8$Simple Confusion Matrix for Tabulation of Two$-$Class Classification Results$$ in Analytics, Data Science, & Artificial Intelligence for reference.

The numbers along the diagonal from the upper left to the lower right $($TP and TN$)$ account for correct decisions of the model, and the other ones $($FN and FP$)$ represent the errors.

Assume that the count of True Positives $($TP$)$ is $50,$ the count of False Positives $($FP$)$ is $10,$ the count of False Negatives $($FN$)$ is $15,$ and the count of True Negatives $($TN$)$ is $75.$

Refer to Table $4\mathrm{.}1,$Common Accuracy Metrics for Classification Models,$$ located in this week's readings.

Use Microsoft$$ Excel$$ to calculate the following ratios for the predictive model described for the formulas:

Accuracy

Precision

True Positive Rate

True Negative Rate

Recall

In a separate document, answer the following questions in $1\mathrm{.}5$ to $2$ pages:

What conclusions can you draw from the above metrics?

What steps might you potentially do to improve the accuracy of the model?