machine learning

Evaluating Classifier Performance

How to Measure Classifier Performance

In the realm of machine learning, evaluating the performance of a classifier is of also importance. It helps us gauge how well our model is performing and identifies areas that may require improvement means they are not making true predictions. In this chapter, we will discuss about key model evaluation metrics for classification tasks. Imagine we have a historical dataset from a telecommunication company, predicting customer churn. We’ve trained a classifier and want to assess its accuracy using the test set. Let’s explore the evaluation metrics in detail i hope you like it if you still have any question in end please ask without any hesitation.

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Classification in Machine Learning

Learn about more on Classification in Machine Learning

Classification is a type of supervised learning in machine learning. It’s like sorting things into categories, or you can say we do a grouping of things. For example, You have a bunch of fruits, and you want to teach a computer how to tell whether each fruit is an apple or a banana. That’s what classification does. In classification, we make computers capable of grouping things based on their features.

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Regression in Machine Learning

More about Regression in Machine Learning

As we discussed Supervised and Unsupervised machine learning models in our previous chapter. If you did not go through it, please first read that chapter so it is feasible for you to understand this chapter about an algorithms of Supervised Machine Learning which is Linear Regression. What is Regression in Machine Learning Let discuss what is regression

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Supervised vs Unsupervised Machine Learning

Supervised vs Unsupervised Machine Learning

As in our previous chapters, we have discussed what is machine learning  and why Python is for machine learning. If you did not go through them, you can. It is recommended. In this chapter, we will learn about Supervised Machine learning & Unsupervised Machine Learning and their key differences. Supervised Machine Learning Now we shall discuss all

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