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Blogs that are actually infographics – providing pictorial depiction of useful knowledge on a topic.
🔹 Short Description: Naive Bayes is a supervised learning algorithm based on Bayes Theorem, assuming feature independence. It’s widely used for classification tasks like spam detection and sentiment analysis. 🔹 Read More …
🔹 Short Description: Bayes Theorem helps compute the probability of an event based on prior knowledge of related conditions. It’s fundamental in probability theory, decision-making, and machine learning. 🔹 Description Read More …
🔹 Short Description: The Gini Index (or Gini Impurity) is a measure of how often a randomly chosen element would be incorrectly classified. It is used by decision tree algorithms Read More …
🔹 Short Description: Information Gain quantifies the reduction in uncertainty achieved by splitting a dataset based on an attribute. It’s widely used in decision tree algorithms like ID3 and C4.5 Read More …
🔹 Short Description: Entropy is a core concept in information theory used to quantify the level of unpredictability or disorder in a system. In machine learning, it plays a pivotal Read More …
🔹 Short Description: The Jaccard Index, also known as the Jaccard Similarity Coefficient, quantifies the similarity between two sets by dividing the size of their intersection by the size of Read More …
🔹 Short Description: The Manhattan Distance formula measures the absolute difference between points across each dimension. It mimics the way you’d move through a city grid—up, down, left, or right—rather Read More …
🔹 Short Description: Euclidean Distance computes the straight-line distance between two points in Euclidean space. It’s a fundamental metric in geometry, machine learning, and clustering tasks. 🔹 Description (Plain Text): Read More …
🔹 Short Description: Cosine Similarity measures the cosine of the angle between two non-zero vectors, helping determine how similar they are regardless of their magnitude. 🔹 Description (Plain Text): Cosine Read More …
🔹 Short Description: TF-IDF (Term Frequency–Inverse Document Frequency) is a statistical formula used to evaluate how important a word is to a document within a collection. 🔹 Description (Plain Text): Read More …