K-nearest neighbors, decision trees, and random forests
Are there other ML algorithms, besides LinearRegression(), that are suitable for the Boston Housing dataset? Absolutely. There are many regressors in the scikit-learn library that may be used. Regressors are a class of ML algorithms that are suitable for continuous target values. In addition to linear regression, Ridge, and Lasso, we can try k-nearest neighbors, decision trees, and random forests. These models perform well on a wide range of datasets. Let’s try them out and analyze them individually.
K-nearest neighbors
The idea behind k-nearest neighbors (KNN) is straightforward. When choosing the output of a row with an unknown label, the prediction is the same as the output of its k-nearest neighbors, where k may be any whole number.
For instance, let’s say that k=3. Given an unknown label, we take n columns for this row and place them in n-dimensional space. Then, we look for the three closest points...