A 'predict' method that predicts the class labels for a set of data points. It calls '_predict_single' method that actually predicts the class label for a single data point.
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Implementing fit method and euclidean distance in KNN
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Next define the "fit" method that takes the training data(X) and it's corresponding labels(y) and stores them. This is the reason why the computation cost is high as it stores all the training data! And also a method to calculates the euclidean distance between two data points.
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K-Nearest Neighbours Algorithm Implemented from Scratch Python
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K-Nearest Neighbours (KNN), implemented from scratch in Python:
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KNNClassifier Class Initialization and K Parameter Selection
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Define the KNNClassifier class and initialize the number of Neighbours (k) to be considered in __init__ method Defining the K value is critical in KNN! A small value of k means noise will have a higher influence on the result and a large value makes it computationally expensive
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Greens, reds, and merge hierarchy decision explanation
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what are the greens and the reds? and how was merge hierarchy decided?
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Claude Sonnet Now Free with Artifacts Feature
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Available for free — look for claude sonnet and don't forget to turn on artifacts.
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Enable Artifacts Feature in Chat Interface
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Click on the experimental feature icon below the chatbar and this window will appear. Turn the artifacts on.
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Binary Quantization Well Explained Discussion
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Well explained binary quantization
Thanks for this thread -
Alignment Lab Releases 600M Reddit Samples Dataset
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Another insane drop from @alignment_lab
: 600M samples from top Reddit posts.