Evaluating a model is important to determine how it performs on the test data or data it has never seen before. You might have heard of the metrics Precision, Recall, and F1-score, but what do these mean?
- TP: True Positive
- FP: False Positive
Accuracy
The ratio of correctly predicted instances to the total number of instances. In other words, it measures how often the model makes the correct prediction. It is used to evaluate the overall performance of a model and is calculated as (TP+TN)/(TP+TN+FP+FN), where the TP ratio of correctly predicted instances to the total number of instances. In other words, it measures how often the model makes the correct prediction. It is used to evaluate the overall performance of a model and is calculated as (TP+TN)/(TP+TN+FP+FN), where TP is truly positive, TN is truly negative, FP is false positive and FN is false negative.
Recall
It is the ratio of correctly predicted positive instances to the total number of actual positive instances. It is also called sensitivity or true positive rate. It measures how well the model can identify positive instances. It is calculated as TP/(TP+FN).
Precision
The ratio of correctly predicted positive instances to the total number of predicted positive examples. It measures how well the model can identify positive instances out of all correctly predicted positive instances. It. It is calculated as TP/(TP+FP).
These metrics are used to evaluate the performance of a classification model in different scenarios. For example, if we have an imbalanced dataset where the positive instances are much less than the negative instances, accuracy alone may not be a good metric to evaluate the model's performance. In such cases, we can use precision and recall to evaluate the model's performance. A high precision indicates that the model is good at identifying positive instances, while a high recall indicates that the model can identify most of the positive instances. The choice of metric depends on the problem at hand and the requirements of the stakeholders.
Example
Import required library or read database file
Input data files are available in the "/" directory.
![Directory]()
Check the database information and also check for any missing values in the data or if the data is NAN values or data type
![NAN values]()
Store other variables in the database and delete some rows.
![Delete]()
Import the visualization library and the seaborn library.
Define the grid color and plot the diagram
![Grid color]()
Check The recall values.
![Recall values]()
After that, we apply drop and fill and check recall value by year of 2016 and print all values.
![Apply]()
Print The 15 Tire Makers with the most Recalls.
![Tire Makers]()
Show by the plot.
![Plot]()
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