Tag Archives: MachineX

MachineX: Understanding FP-Tree construction


In my previous blog, MachineX: Why no one uses apriori algorithm for association rule learning?, we discussed one of the first algorithms in association rule learning, apriori algorithm. Although even after being so simple and clear, it has some weaknesses … Continue reading

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


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 … Continue reading

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MachineX: Two parts of Association Rule Learning


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 … Continue reading

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MachineX: Choosing Support Vector Machine over other classifiers


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 … Continue reading

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MachineX: One more step towards NAIVE BAYES


I hope we understand the conditional probabilities and Bayes theorem through our previous blog. Now let’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 … Continue reading

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MachineX: Unfolding Mystery Behind NAIVE BAYES CLASSIFIER


In machine learning, Naive Bayes classifiers are a family of simple “probabilistic classifiers “based on applying Bayes’ theorem with strong (naive) 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 … Continue reading

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