In the project on gun violence in the United States of America
between 1992 to 2022, various statistical analysis techniques have
been used. Statistical analysis and machine learning techniques play
a vital role in the visualization of data. R is one of the key statistical
analysis techniques that has been used in the project.
The different statistical analysis techniques were evaluated on the
basis of a number of factors. The main criteria used for the
evaluation of the techniques were accuracy, precision, as well as
recall (Jordan, 2018). Accuracy refers to the per cent of correct
predictions made by the statistical analysis technique for the test
data. In order to find the accuracy of the technique, the correct
number of predictions was divided by the total number of
predictions. Precision can be defined as the ability of the technique
to deliver consistent results. It refers to the percentage of true
positive examples among all the examples that have been predicted
by the statistical analysis technique. It can be calculated by dividing
the true positives by the total of true positives and false positives.
Recall is another important aspect that has been used in order to
evaluate the statistical analysis technique used in the project on gun
violence in the United States of America.
From the evaluation, it was found that the accuracy level of the R
model is about 87.8 per cent. The models that have an accuracy
greater than 70 per cent are considered to be ideal for a project. The
models with accuracy between 70 to 90 per cent are considered to
be realistic. Therefore, the R model is an ideal statistical analysis tool
for the gun violence project. Moreover, the precision, as well as
recall of the R model, is found to be high. It ensures that the use of
the R model can enhance the data visualization of the project.
References
Jordan, J. (2018, August 25).
Evaluating a machine learning model.
Jeremy Jordan. Retrieved July 18, 2022, from
https://www.jeremyjordan.me/evaluating-a-machine-learning-
model/
