Classification Methods

Classification Methods#

List basic notes on various types of models that can be used for Classification tasks

  • most of these will assume you have already split data into

    • X: features (inputs)

    • y: target (output)

k-Nearest Neighbors (knn)#

  • binary output like “will this customer leave (1) or stay (0) based on account age and customer service call count”

  • sets the result for the target based on nearby points (must be an odd number of points to prevent a “tie”)

  • it determines “nearness” by mapping the inputs in vector space and computing the length of the vector between each training data point and test data point

from sklearn.neighbors import KNeighborsClassifier
# Create a KNN classifier with 6 neighbors
knn = KNeighborsClassifier(n_neighbors=6)
# Fit the classifier to the data
knn.fit(X, y)
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