Here's a situation you've got into: You are working on a classification problem and you have generated your set of hypothesis, created features and discussed the importance of variables. Within an hour, stakeholders want to see the first cut of the model. You have hunderds of thousands of data points and quite a few variables in your training data set. In such situation, if I were at your place, I would have used'Naive Bayes', which can be extremely fast relative to other classification algorithms. It works on Bayes theorem of probability to predict the class of unknown data set.

You are working on a classification problem and you have generated your set of hypothesis, created features and discussed the importance of variables. Within an hour, stakeholders want to see the first cut of the model. You have hunderds of thousands of data points and quite a few variables in your training data set. In such situation, if I were at your place, I would have used'Naive Bayes', which can be extremely fast relative to other classification algorithms. It works on Bayes theorem of probability to predict the class of unknown data set.

Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes' probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. In this first part of a series, we will take a look at the theory of naive Bayes classifiers and introduce the basic concepts of text classification. In following articles, we will implement those concepts to train a naive Bayes spam filter and apply naive Bayes to song classification based on lyrics. Starting more than half a century ago, scientists became very serious about addressing the question: "Can we build a model that learns from available data and automatically makes the right decisions and predictions?" Looking back, this sounds almost like a rhetoric question, and the answer can be found in numerous applications that are emerging from the fields of pattern classification, machine learning, and artificial intelligence. Data from various sensoring devices combined with powerful learning algorithms and domain knowledge led to many great inventions that we now take for granted in our everyday life: Internet queries via search engines like Google, text recognition at the post office, barcode scanners at the supermarket, the diagnosis of diseases, speech recognition by Siri or Google Now on our mobile phone, just to name a few.

Linear regression is perhaps one of the most well-known and well-understood algorithms in statistics and machine learning. Predictive modeling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explainability. We will borrow, reuse and steal algorithms from many different fields, including statistics and use them towards these ends. The representation of linear regression is an equation that describes a line that best fits the relationship between the input variables (x) and the output variables (y), by finding specific weightings for the input variables called coefficients (B). We will predict y given the input x and the goal of the linear regression learning algorithm is to find the values for the coefficients B0 and B1.

Weka makes a large number of classification algorithms available. The large number of machine learning algorithms available is one of the benefits of using the Weka platform to work through your machine learning problems. In this post you will discover how to use 5 top machine learning algorithms in Weka. How To Use Classification Machine Learning Algorithms in Weka Photo by Don Graham, some rights reserved. We are going to take a tour of 5 top classification algorithms in Weka.