Naive Bayes classifiers are a group of machine learning models that use Bayes’ Theorem to work well. They are meant to help with classification problems by figuring out how likely it is that a data point belongs to a certain group. The “naive” part comes from the idea that all input features are separate, which makes the math a lot easier.
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The K-nearest neighbor(knn) algorithm is a learning method that classifies data based on the “proximity” principle. By utilising the k-nearest neighbor algorithm formula, specifically Euclidean distance, the KNN model identifies the most similar data points to make predictions. This k-nearest neighbor algorithm working process involves a majority vote for...
One decision tree in machine learning is easy to follow, but it can be biased or too sensitive to certain data points. In random forest algorithm in machine learning is like a “committee of experts” because it has many trees that vote on an outcome and the one with the most votes wins. When students first start learning about data science, the switch from a single tree to a r...
You might be surprised at how machines really “think” about a problem. This is where the idea of a decision tree in machine learning model comes in. It is one of the most user-friendly AI tools because it turns complicated datasets into easy-to-read options. If you want to learn how to use predictive analytics, the first thing you need to do is understand this algorith...
Logistic regression is one of the most reliable and commonly used tools in the large toolbox of supervised machine learning. Many people think it has to do with predicting numerical trends because of its name, but it is actually a key part of logistic regression in machine learning classification. This algorithm doesn’t try to guess a range of numbers; instead, it looks at ho...