Nevertheless, the Naive Bayes algorithm has been shown time and time again to perform really well in classification problems, despite the assumption of independence. Simultaneously, it is a fast algorithm since it scales easily to include many predictors without having to handle multi-dimensional corre...
1.5. Naive Bayes: Naive Bayes is a probabilistic machine learning algorithm commonly used for classification tasks, especially in natural language processing and text analysis. It’s based on Bayes’ theorem and makes predictions by calculating the probability of a data point belonging to a certain...
In this way, the algorithm would perform a classification of the images. That is, in machine learning, a programmer must intervene directly in the action for the model to come to a conclusion.In the case of a deep learning model, the feature extraction step is completely unnecessary. The ...
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How does sentiment analysis work? In data science lingo, sentiment analysis is a classification problem: the algorithm is presented with pieces of text that need to be classified as positive, negative, or neutral. The problem is usually tackled with the help of Natural Language Processing (NLP)...
There are many other other online courses you can take after this one (see My answer to What is the best MOOC to get started in Machine Learning?)but at this point you are mostly ready to go to the next step. Implement an algorithm My recommended next step is the following. Get a ...
However, the Naive Bayes algorithm achieves an F-Measure score of 93% for the same category. Similarly for the Collaborator category the Naive Bayes model achieved better results compared to the Random Forest model. Both categories can contain similar information, i.e., the name of other ...
Supervised Learning Algorithm Examples Here are some examples of different supervised learning algorithms and what they are used for: Linear regressionLogistic regressionDecision treeRandom forestSupport vector machines (SVMs)K-nearest neighbors (KNN)Naive Bayes ...
The super learner is an ensemble machine learning algorithm that combines all of the models and model configurations that you might investigate for a predictive modeling problem and uses them to make a prediction as-good-as or better than any single model that you may have investigated. The supe...
Generally, when working on a machine learning problem you cannot know which algorithm will be the best for your problem beforehand. If you had enough information to know which algorithm would achieve the best performance, you probably would not be doing applied machine learning. You would be doin...