Regression vs Classification Multi Layer Perceptrons (MLPs) _ day 10
Regression with Multi-Layer Perceptrons (MLPs) Introduction Neural networks, particularly Multi-Layer Perceptrons (MLPs), are essential tools in machine learning for solving both regression and classification problems. This guide will provide a detailed explanation of MLPs, covering their structure, activation functions, and implementation using Scikit-Learn. Regression vs. Classification: Key Differences Regression Objective: Predict continuous values. Output: Single or multiple continuous values. Example: Predicting house prices, stock prices, or temperature. Classification Objective: Predict discrete class labels. Output: Class probabilities or specific class labels. Example: Classifying emails as spam or not spam, recognizing handwritten digits, or identifying types of animals in images. Regression with MLPs MLPs can be utilized for regression tasks, predicting continuous outcomes. Let’s walk through the implementation using the California housing dataset. Activation Functions in Regression MLPs In regression tasks, MLPs typically use non-linear activation functions like ReLU in the hidden layers to capture complex patterns in the data. The output layer may use a linear activation function to predict continuous values. Fetching and Preparing the Data from sklearn.datasets import fetch_california_housing from sklearn.model_selection import train_test_split # Load the California housing dataset housing = fetch_california_housing() # Split the data into training, validation, and test sets X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, housing.target,...
