Classification algorithms try to predict the class or the label of the categorical target variable. A categorical variable typically represents qualitative data that has discrete values, such as pass/fail or low/medium/high, etc. Out of the many classification algorithms, the Naïve Bayes classifier is one of the simplest classification algorithms. The Naïve Bayes classifier is often used with large text datasets among other applications. The aim of this article is to explain how the Naive Bayes algorithm works.
In today's world, Machine learning becomes one of the popular and exciting fields of study that gives machines the ability to learn and become more accurate at predicting outcomes for the unseen data i.e, not seen the data in prior. The ideas in Machine learning overlaps and receives from Artificial Intelligence and many other related technologies. Today, machine learning is evolved from Pattern Recognition and the concept that computers can learn without being explicitly programmed to performing specific tasks. We can use the Machine Learning algorithms(e.g, Machine learning models can be classified into two types of models – Discriminative and Generative models.
This article was published as a part of the Data Science Blogathon. In this blog, we will be discussing how to perform image classification using four popular machine learning algorithms namely, Random Forest Classifier, KNN, Decision Tree Classifier, and Naive Bayes classifier. We will directly jump into implementation step-by-step. At the end of the article, you will understand why Deep Learning is preferred for image classification. However, the work demonstrated here will help serve research purposes if one desires to compare their CNN image classifier model with some machine learning algorithms.
MIT press provides another excellent book in creative commons. I plan to buy it and I recommend you do. This book provides a broad introduction to algorithms for decision making under uncertainty. An agent is an entity that acts based on observations of its environment. The interaction between the agent and the environment follows an observe-act cycle or loop.
Bayesian nonparametric (BNP) models are a prominent tool for performing flexible inference with a natural quantification of uncertainty. Notable examples for \(T\) include normalization for random probabilities (Regazzini et al., 2003), kernel mixtures for densities (Lo, 1984) and for hazards (Dykstra and Laud, 1981; James, 2005), exponential transformations for survival functions (Doksum, 1974) and cumulative transformations for cumulative hazards (Hjort, 1990). Very often, though, the data presents some structural heterogeneity one should carefully take into account, especially when analyzing data from different sources that are related in some way. For instance this happens in the study of clinical trials of a COVID-19 vaccine in different countries or when understanding the effects of a certain policy adopted by multiple regions. In these cases, besides modeling heterogeneity, one further aims at introducing some probabilistic mechanism that allows for borrowing information across different studies.
Bayesian statistics is all about belief. We have some prior belief about the true model, and we combine that with the likelihood of our data to get our posterior belief about the true model. In some cases, we have knowledge about our domain before we see any of the data. Bayesian inference provides a straightforward way to encode that belief into a prior probability distribution. For example, say I am an economist predicting the effects of interest rates on tech stock price changes.
What the heck is it? In Quantum state the parameters like Entropy and temperature impact are observed. Strange thing: It is a model but no output nodes. If you known about ml, simply we have a output and based upon the different learning rule such as gradient descend we learn the values for parameters for weight, and other parameters.(calling it as a learning model) The hidden nodes learn or map the things from given input represented by v in above image. It falls under unsupervised learning as you know it.
As stated in the general overview, we need to calculate the summary statistics for each class (and feature) as well as the prior. First of all, we need to gather some basic information about the dataset and create three zero-matrices to store the mean, the variance, and the prior for each class. Next, we iterate over all the classes, compute the statistics and update our zero-matrices accordingly. For example, assume we have two unique classes (0,1) and two features in our dataset. The matrix storing the mean values, therefore will have a two rows and two columns (2x2). The prior is just a single vector (1x2), containing the ratio of a single classes' samples divided by the total sample size.
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Life is uncertain, and statistics can help us quantify certainty in this uncertain world by applying the concepts of probability and inference.