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accuracy is a common metric used to evaluate the performance of machine learning models. it measures the ratio of correctly predicted instances to the total instances in the dataset. in this tutorial, we will learn how to calculate accuracy in python using a simple example.
make sure you have python installed on your system. you can download it from python.org. additionally, we'll use the scikit-learn library, so you can install it using:
accuracy is calculated using the formula:
accuracy=
total number of predictions
number of correct predictions
×100
let's consider a basic example of a classification problem using the popular iris dataset.
in this example, we load the iris dataset, split it into training and testing sets, initialize a k-nearest neighbors classifier, train the classifier, make predictions on the test set, and finally, calculate and print the accuracy.
calculating accuracy is a fundamental step in evaluating the performance of machine learning models. in this tutorial, we demonstrated how to calculate accuracy using a simple example with the iris dataset. you can apply a similar approach to your own machine learning projects. remember to explore other evaluation metrics depending on your specific use case and model requirements.
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