Neural Networks in SQL Server Analysis Services
Neural network models in SQL Server Analysis Services are based on the Multilayer Perceptron (MLP) algorithm. They are powerful tools for classification and prediction tasks, capable of modeling complex relationships in data.
Overview
Neural networks are inspired by the structure of the human brain, consisting of interconnected nodes (neurons) organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer. The connections between neurons have weights that are adjusted during the training process to minimize prediction errors.
Key Concepts
- Neurons: Basic computational units that receive inputs, perform a weighted sum, and apply an activation function.
- Layers: Input layer, hidden layers, and output layer. The number of hidden layers and neurons per layer can significantly impact model performance.
- Weights: Numerical values associated with connections between neurons, representing the strength of the connection.
- Activation Function: A non-linear function applied to the weighted sum of inputs to determine the output of a neuron. Common functions include Sigmoid and TanH.
- Backpropagation: The algorithm used to train neural networks by adjusting weights based on the error in the output.
When to Use Neural Networks
Neural networks are well-suited for:
- Problems where the relationships between variables are complex and non-linear.
- Large datasets with many features.
- Classification tasks (e.g., predicting customer churn, identifying fraudulent transactions).
- Regression tasks (e.g., predicting sales figures, forecasting stock prices).
Note: Neural networks can be computationally intensive to train and may require careful tuning of parameters to achieve optimal results.
Algorithm Parameters
The neural network algorithm in SQL Server Analysis Services offers several parameters to control its behavior:
HIDDEN_LAYERS: Specifies the number of hidden layers and the number of neurons in each layer.MAX_OUTPUT_ দেবে: Controls the maximum number of output neurons for a classification model.OPTIMIZATION_GOAL: Determines whether to optimize for accuracy or speed.
Example of Setting Parameters (DMX)
ALTER MINING MODEL [MyNeuralNetworkModel]
ADD MINING TECHNOLOGY MINE_CAPTION = 'My Neural Network Model',
MINING_ALGORITHM = 'NET_ a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a'''
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Using the Neural Network Algorithm
To create and use a neural network model, you typically follow these steps:
- Define a Mining Structure: Specify the columns from your data source view that will be used as input and predictable attributes.
- Create a Mining Model: Associate the neural network algorithm with the mining structure.
- Train the Model: Use DMX or the SQL Server Data Tools (SSDT) to train the model on your data.
- Explore and Query: Use mining model viewers and DMX queries to understand the model's behavior and make predictions.
Tip: For classification problems, ensure your predictable attribute is discrete. For regression, it should be continuous.
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