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K-Means Clustering: Automatically Finding Groups in Data
access_time 2025-04-26T13:12:12.783Z face Nerchuko
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: The Power of Many Trees
access_time 2025-04-26T13:03:39.16Z face Nerchuko
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 Trees for Classification: Making Choices Like a Flowchart
access_time 2025-04-26T12:56:44.185Z face Nerchuko
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...
Naive Bayes Classifier Explained (Part 2)
access_time 2025-04-26T12:49:02.491Z face Nerchuko
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...
Naive Bayes Classifier Explained (Part 1)
access_time 2025-04-26T12:40:25.008Z face Nerchuko
Naive Bayes Classifier Explained (Part 1) Understanding the power of probability for classifying data. Naive Bayes Classifier Explained Imagine you're a doctor diagnosing a patient. You look at their symptoms (features) and use your past experience (training data) and medical knowledge to estimate t...