How can we visually determine the class to which a new point belongs using the scatter plot?
In the field of machine learning, one popular algorithm for classification tasks is the K nearest neighbors (KNN) algorithm. This algorithm classifies new data points based on their proximity to existing data points in a training dataset. One way to visually determine the class to which a new point belongs using a scatter plot is by examining the distribution of the existing data points and their corresponding classes.
To illustrate this process, let's consider a simple example. Suppose we have a dataset with two features, feature A and feature B, and two classes, class 0 and class 1. We can create a scatter plot where the x-axis represents feature A and the y-axis represents feature B. Each data point is plotted according to its feature values, and is colored based on its class label.
Now, let's say we have a new data point with feature values A_new and B_new. To determine the class of this new point, we can visualize its position on the scatter plot. We calculate its Euclidean distance to all the existing data points in the training dataset. The K nearest neighbors of the new point are the K data points with the shortest distances to the new point.
Next, we examine the classes of these K nearest neighbors. If the majority of the K nearest neighbors belong to class 0, we classify the new point as class 0. Similarly, if the majority of the K nearest neighbors belong to class 1, we classify the new point as class 1.
It is important to note that the choice of K is a hyperparameter that needs to be determined. A smaller value of K may result in a more flexible decision boundary, but it can also lead to overfitting. On the other hand, a larger value of K may result in a smoother decision boundary, but it can also lead to underfitting. Therefore, it is common practice to experiment with different values of K and select the one that performs best on a validation dataset.
Visually determining the class to which a new point belongs using a scatter plot involves calculating the distances between the new point and the existing data points, identifying the K nearest neighbors, and classifying the new point based on the majority class of the K nearest neighbors.
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