MLP (Multi-Layer Perceptron) Neural Network in Regression

MLP (Multi-Layer Perceptron) Neural Network in Regression

MLP (Multi-Layer Perceptron) Neural Network in Regression

Description

An MLP neural network for regression is designed to predict continuous numerical values. It consists of multiple layers, including an input layer, one or more hidden layers, and an output layer. Each layer contains neurons that process the input data and apply activation functions. The network's weights and biases are adjusted during training to minimize the difference between predicted and actual values.

Why to use

  1. Non-Linear Mapping
  2. Flexibility
  3. Universal Approximation
  4. Feature Learning
  5. Generalization

When to use

  1. Non-linear Relationships
  2. Complex Data Patterns
  3. High-Dimensional Data
  4. Unstructured Data
  5. Feature Learning
  6. Availability of Sufficient Data

When not to use

  1. Linear Relationships
  2. Low-Dimensional Data
  3. Time and Resource Constraints
  4. Interpretable models
  5. Noisy Data

Prerequisites

  1. Feature Selection or Extraction
  2. Labelled Training Data
  3. Data Split
  4. Data Pre-processing
  5. Architecture Design
  6. Hyperparameter Tuning
  7. Training and optimization
  8. Regularization and Early Stopping
  9. Evaluation and Testing

Input

Choose a continuous dependent variable (column) and select a specific number (n) of independent variables (columns) with either categorical or numerical types.

Output

  1. AIC
  2. BIC
  3. Adjusted R Square
  4. R Square
  5. RMSE
Statistical Methods Used
  1. Mean Squared Error (MSE)
  2. Mean Absolute Error (MAE)
  3. R-Squared
  4. Cross-Validation
  5. Feature Importance
  6. Model Selection
  7. Hypothesis
Limitation
  1. Overfitting
  2. Computational Complexity
  3. Selection of Hyperparameters
  4. Interpretability
  5. Data Requirements
  6. Lack of Uncertainty Estimation

An MLP (Multi-Layer Perceptron) neural network in regression refers to a specific type of neural network architecture used for regression tasks. The MLP is designed to predict continuous numerical values as the output.
The MLP consists of multiple layers, including an input layer, one or more hidden layers, and an output layer. Each layer contains a set of interconnected neurons, also known as perceptrons or nodes.

The MLP learns to map the input features to the desired continuous output by adjusting the weights and biases associated with the connections between neurons. These adjustments are made iteratively using optimization algorithms, such as backpropagation, to minimize the difference between the predicted output and the actual target values.

Each neuron in the hidden layers and the output layer applies an activation function to the weighted sum of its inputs. The activation function introduces non-linearities into the model, allowing it to capture complex relationships and patterns in the data.
The MLP can be used to make predictions on new, unseen data. The input features are fed into the network and propagated through the layers. The output value is obtained from the output neuron of the MLP.

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