Unlocking Predictive Power Through Binary SimplicityLike several algorithm in machine learning, Bernoulli Naive Bayes has its strengths and limitations.Simplicity: Easy to implement and understand.Efficiency: Fast to coach and predict, works well with large feature...
Boosting Your Method to SuccessImagine running a relay race. Each runner improves upon the previous one’s performance, and together, they win the race. That’s how these algorithms work — every latest model compensates for...
The event models described above will also be combined in case we have now a heterogenous data set, i.e., an information set that incorporates several types of features (for instance, each categorical and continuous...
Tips on how to approach a real-world problem? Bringing mathematical theorems & data togetherConditional ProbabilityProblem 1Problem 2The law of total probabilityProblem 3Bayes’ TheoremWhen to use Conditional Probability & when to use Bayes’ Theorem?Okay. You...