Support Vector Machine, which is a supervised machine learning algorithm used for classification and regression tasks. It works by finding the hyperplane that best separates the classes in the feature space and it aims to maximize the margin between the classes while minimizing the classification error.
Imagine you have a collection of data points, each with multiple features.These data points belong to one of two categories,let's say category A and category B. SVM helps you find the best way to separate these two categories in the feature space.
In essence, SVM is a powerful algorithm for classification tasks that works by finding the best way to separate different categories of data points while maximizing the margin between them.
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from sklearn.svm import svc SVC : Support Vector Classification ; is used when dealing with classification problems, where the goal is to classify data points into one of two or more categories
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from sklearn.neighbors import KNeighborsClassifier By importing KNeighborsClassifier from sklearn.neighbors, you can create KNN models for classification tasks in your Python code. After importing, you can instantiate a KNeighborsClassifier object, set its parameters (such as the number of neighbors, distance metric, etc.), fit it to your training data, and use it to make predictions on new data.