This paper investigates the consequences of encoding a $K$-valued categorical variable incorrectly as $K$ bits via one-hot encoding, when using a Na\"{\i}ve Bayes classifier. This gives rise to a product-of-Bernoullis (PoB) assumption, rather than the correct categorical Na\"{\i}ve Bayes classifier. The differences between the two classifiers are analysed mathematically and experimentally. In our experiments using probability vectors drawn from a Dirichlet distribution, the two classifiers are found to agree on the maximum a posteriori class label for most cases, although the posterior probabilities are usually greater for the PoB case.

Naive Bayes Classifiers are a family of algorithms that are based on Bayes theorem. They are classification algorithms with the main principle being that every feature being classified is independent of the other. P(c) Prior, number of documents in class c divided by the total number of documents. We have the following documents. Document 1 and 2 are Burgers while 3 and 4 are Sandwich.

Linear Regression – Simple Linear Regression- there is only independent variable. Multiple Linear Regression- refers to defining a relationship between independent and dependent variables Logistic Regression – A super simple form of regression analysis in which the outcome variable is binary or dichotomous. Helps to estimate adjusted prevalence rates, adjusted for potential confounders (sociodemographic or clinical characteristics) Linear Discriminant Analysis – A generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. Classification and Regression Trees- Decision trees are are an important type of algorithm for predictive modeling machine learning. A greedy algorithm based on divide and conquer rule.

Complex equations can be calculated faster as ever and everybody can start experimenting with machine learning. Do want to give it a try too? This blog is a very simple primer into the exciting world of machine learning and comes with a working demo written in Node. Machine learning is the art of using computer algorithms to learn from experiences and use those experiences for future predictions. Tom Mitchell gave a really simple definition of machine learning.

Machine learning is the art of using computer algorithms to learn from experiences and use those experiences for future predictions. Tom Mitchell gave a really simple definition of machine learning. A computer program is said to learn from experience (E) with respect to some task (T) and some performance measure (P), if its performance on (T), as measured by (P), improves with experience (E). This definition dazzled me a bit, too. In human language, if you want your program to predict, for example, buy patterns at a busy grocery store (task T), you can run it through a Machine Learning algorithm with data about past buying patterns (experience E) and, if it has successfully learned, it will then do better at predicting future buy patterns (performance measure P).

In case you've been hanging out with the technology groundhog in its cave: we're in the midst of an AI spring. The past two years have seen a resurgence in excitement around our ability to model human-like intelligence in computer algorithms. This excitement has a number of catalysts, not least of which is the enabled application of deep neural networks to a multitude of fields by the advancement of Moore's law. For the average person, the AI spring is a period of unbridled excitement: your iPhone will transcribe your voicemails so you don't have to lift the phone to your ear, Facebook will translate the posts of the friends you made during that one summer in college in Puerto Rico, and your Alexa can tell you interesting trivia about Star Wars. But the life of a startup CEO is not so simple.