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Why do we use hyperparameter tuning for Machine Learning models?

Kayley Marshall
Kayley MarshallAnswered

Parameters and hyperparameters must be distinguished in Machine Learning. An algorithm for learning discovers or estimates model parameters for a given data collection, then continues to update these values as it continues to learn. These settings are added to the model after the learning process is complete. As an illustration, a neural network’s weights and biases are both examples of parameters.

On the other hand, hyperparameters are algorithm-specific, so we cannot determine their values from the data. We compute the model parameters using hyperparameters. The model parameters for a particular data set are affected by the hyperparameters.

Finding the appropriate hyperparameter settings for a learning algorithm and applying this improved algorithm to any data set is hyperparameter tuning in deep learning. This combination of hyperparameters enhances the performance of the model by minimizing a predetermined loss function to deliver more accurate outputs with fewer mistakes. Bear in mind that the learning algorithm strives to discover the best possible solution within the constraints of the current configuration by optimizing the loss based on the input data. However, hyperparameters precisely define this arrangement.

  • A crucial aspect of regulating the performance of an ML model is algorithm tuning. If we do not appropriately tweak our hyperparameters, our predicted model parameters will not minimize the loss function, resulting in poor outcomes. This indicates our method made more mistakes. In reality, crucial metrics such as the matrix of accuracy and confusion will be worse.

Important Machine Learning hyperparameter tuning in neural networks include:

Count of concealed layers. A trade-off exists between making our neural network as basic as feasible (fast and generalized) and accurately identifying our input data. We may begin with values between four and six and evaluate the accuracy of our data predictions as we raise or decrease this hyperparameter.

Learning rate. Model parameters are repeatedly modified, and the learning rate determines the magnitude of each alteration. The slower the learning rate, the less the parameter estimations will change. It takes longer (and more data) for the model to fit, but it increases the likelihood for the least loss to be found.

Momentum to avoid local minima by opposing fast parameter value changes. As a result, parameters are more likely to continue heading in the direction they were previously moving rather than zigzag between iterations. Aim for low initial momentum levels and adjust upwards as necessary.

A number of neurons and nodes per layer: When deciding the number of neurons per layer, more is not necessarily better. Increasing the number of neurons may be beneficial, up to a point. However, overly large layers may remember the training dataset, leading the network’s accuracy on fresh data to suffer.

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