The Naïve Bayes classifier is a supervised machine learning algorithm that is used for classification tasks such as text classification.
Thek-nearest neighbor (KNN)algorithm is another widely used classification method. Although it can be applied to both regression and classification tasks, it is most commonly used for classification. The algorithm assigns a class to a new data point based on the classes of its k nearest neighbors...
A classification algorithm is a categorization-focusedmachine learning algorithmthat sorts input data into different classes or categories.Artificial intelligence (AI)models use classification algorithms to process input datasets against a specified classifier that sets the criteria for how the data should b...
Naive Bayes is a supervised machine learning algorithm. As the name implies it’s based on Bayes theorem. In this post, you will discover what’s happening behind the Naive Bayes classifier when you are dealing with continuous predictor variables. Here I have used R language for coding. Let ...
Self-trained Naive Bayes classifier: This can help you easily and quickly predict the class of the test data set. It also performs well in multi-class prediction. Generative adversarial networks (GAN): These are algorithmic architectures that utilise two neural networks, pitting one against the ot...
“master algorithm:” backpropagation Evolutionaries whereas connectionism is about fine-tuning the brain, evolution is about creating the brain “master algorithm:” genetic programming Bayesians based on probabilistic inference, i.e., incorporating a priori knowledge: certain outcomes are more likely ...
The choice of kernel function for an SVM algorithm is a tradeoff between accuracy and complexity. The more powerful kernel functions, such as the RBF kernel, can achieve higher accuracy than the simpler kernel functions, but they also require more data and computation time to train the SVM algo...
Q13: Can you think what happens to the training as the value of k in kNN is changed? What happens when k=1? What about when k is very large? Q14: How does the accuracy change as you vary k? Q15: Do you understand how the naïve Bayes algorithm works? Q16: What observations ca...
” For “relatively” very small dataset sizes, I’d recommend comparing the performance of a discriminative Logistic Regression model to a related Naive Bayes classifier (a generative model) or SVMs, which may be less susceptible to noise and outlier points. Even so, logistic regression is a ...
Naive Bayes: An algorithm that uses Bayes’ theorem to categorize words in a block of text. Support vector machines: A fast and efficient classification algorithm used to solve two-group classification problems. Deep learning (DL): Also known as an artificial neural network, deep learning is an...