Hierarchical Clustering Explained: Building Clusters Step-by-Step Understand how data groups emerge by merging or splitting, visualized with dendrograms. Hierarchical Clustering: Building Clusters Like a Family Tree Imagine organizing items not just into separate boxes (like K-Means does), but creat...
K-Means Clustering Explained: Finding Groups in Your Data Learn how this popular algorithm automatically groups similar data points together. K-Means Clustering: Automatically Finding Groups in Data Imagine you have a big pile of customer data – their spending habits, age, income, etc. How can you a...
Random Forest Classification Explained Unlock Accurate Predictions by Harnessing the Power of Many Trees. Random Forest Classification: The Power of Many Trees We know Decision Trees can classify data by asking questions. But sometimes, a single tree can be too sensitive to the specific training dat...
Decision Tree Classification Explained Simply Learn how machines make decisions like a flowchart using Entropy & Information Gain. Decision Trees for Classification: Making Choices Like a Flowchart How do we make decisions in everyday life? Often, we ask a series of questions. "Is it raining?" -> If...
Gaussian Naive Bayes Explained (Part 2: Continuous Data) Learn how Naive Bayes handles features like Age or Salary using the Bell Curve. Gaussian Naive Bayes: Handling Numbers in Naive Bayes In Part 1, we saw how the Naive Bayes classifier uses probabilities based on feature frequencies (like counti...