Random forests use an ensemble learning method for classification or regression. A random forest classifier is used to solve classification problems. When we train a random forest with training data, it generates several decision trees. And then, when input features...
In our previous articles, we discussed how to solve regression problems using regression trees and classification problems using classification trees. We can also plot decision trees using the plot_tree() function from the sklearn.tree module. In this article, we will...
Decision trees use a supervised learning approach. They can be used for regression or classification problems. A regression tree is used to solve regression problems. And a classification tree is used to solve classification problems. In a classification tree, the...
Linear Discriminant Analysis (LDA) is used to solve multiclass classification problems in machine learning. Let’s say we have two-dimensional data points. In LDA, we create a new axis and plot the data points on the new axis such that: The distance between the means...
The k-nearest neighbors (KNN) algorithm is a supervised learning method. The method can be used for solving regression or classification problems. The k-nearest neighbors regressor is used for solving regression problems, and the k-nearest neighbors classifier is used...
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