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# MachineX

## MachineX: Why no one uses apriori algorithm for association rule learning?

Reading Time: 3 minutes In my previous blog, MachineX: Two parts of Association Rule Learning, we discussed that there are two parts in performing association rule learning, namely, frequent itemset generation and rule generation. In this blog, we are going to talk about one of the algorithms for frequent itemset generation, viz., Apriori algorithm. The Apriori Principle Apriori algorithm uses the support measure to eliminate the itemsets with low Continue Reading

## MachineX: Two parts of Association Rule Learning

Reading Time: 2 minutes In our previous blog, MachineX: Layman guide to Association Rule Learning, we discussed what Association rule learning is all about. And as you can already tell, with a large dataset, which almost every market has, finding association rules isn’t very easy. For these, purposes, we introducedÂ measures of interestingness, which wereÂ support, confidenceÂ andÂ lift. Support tells us how frequent an itemset is in a given dataset and confidence Continue Reading

## MachineX: Choosing Support Vector Machine over other classifiers

Reading Time: 4 minutes When one has to select the best classifier from all the good options, often he would be on the horns of a dilemma. Decision tree/Random Forest, ANN, KNN, Logistic regression etc. are some options that often used for the choice of classification. Every one of it has its pros and cons and when to select the best one the probably the most important thing to Continue Reading

## MachineX: One more step towards NAIVE BAYES

Reading Time: 4 minutes I hope we understand the conditionalÂ probabilities and Bayes theorem through our previous blog. Now letâ€™s use this understanding to find out more about the naive Bayes classifier. NAIVE BAYES CLASSIFIER Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. Naive Bayes Continue Reading

## MachineX: Unfolding Mystery Behind NAIVE BAYES CLASSIFIER

Reading Time: 4 minutes InÂ machine learning, Naive Bayes classifiersÂ are a family of simpleÂ “probabilistic classifiersÂ “based on applyingÂ Bayes’ theoremÂ with strong (naive)Â independence assumptions between the features. The Naive Bayes Classifier technique is based on the so-called Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Despite its simplicity, Naive Bayes can often outperform more sophisticated classification methods.   In simple terms, a Naive Bayes classifier assumes that Continue Reading