https://bergstrom.org/tags/scikit-learn/Scikit-Learn - Tag - Jorgen BergstromHugo -- gohugo.ioen-usJorgen BergstromTue, 28 May 2024 00:00:00 -0500https://bergstrom.org/posts/nn_hammer_distance_sklearn/Tue, 28 May 2024 00:00:00 -0500Jorgen Bergstromhttps://bergstrom.org/posts/nn_hammer_distance_sklearn/Summary The MLPRegressor in scikit-learn is a powerful tool for performing regression tasks using a multi-layer perceptron (MLP), which is a type of artificial neural network. It is a supervised learning algorithm that learns a function that maps input data to continuous output values. It can model complex relationships between features and the target variable. Code Location You can find the code in this example here. import matplotlib.pyplot as plt import numpy as np from sklearn.https://bergstrom.org/posts/regression_hammer_distance_sklearn/Mon, 27 May 2024 00:00:00 -0500Jorgen Bergstromhttps://bergstrom.org/posts/regression_hammer_distance_sklearn/Linear Regression Linear regression is a fundamental statistical method used in machine learning to model the relationship between a dependent variable (often called the target or outcome) and one or more independent variables (often called features or predictors). In our example of hammer throwing we have 2 inputs (velocity and angle), and one output (distance). The regression equation therefore becomes: $d = b_0 + b_1 v + b_2 \alpha$. The goal of linear regression is to find the values of the coefficients ($b_i$) that minimize the difference between the predicted values and the actual values.