{"slug": "lesson-7-understanding-the-role-of-bias-parameters-and-hyperparameters-in", "title": "Lesson 7: Understanding the Role of Bias, Parameters and Hyperparameters in Machine Learning", "summary": "A machine learning tutorial explains that traditional machine learning models saturate because they reach theoretical limits and cannot extract more information from additional data, while large neural networks with 20–25 hidden layers overcome this by learning complex patterns. The lesson also defines bias as a parameter that shifts the activation function, noting that extreme bias values can cause issues such as outputs near zero or large positive values.", "body_md": "Understanding the Role of Bias, Parameters and Hyperparameters in Machine Learning\n\nLet’s discuss what is Traditional Machine Learning, Small Neural Network & Large Neural Network.\n\nTraditional Machine Learning:\n\nTraditional machine learning relies on hand-crafted features and algorithms to detect patterns in data. It requires large amounts of data to achieve high performance, and after a point of time its performance begins to saturate.\n\nSmall Neural Network:\n\nSmall neural networks typically contain 1–2hidden layers and are used to identify small patterns and relations in data. They require less data than traditional machine learning and can achieve high accuracy with smaller datasets.\n\nLarge Neural Network:\n\nLarge neural networks contain 20–25hidden layers, which enable them to detect complex patterns and relationships in large datasets. These networks require significant computational resources and training time but can achieve much higher performance than traditional machine learning or small neural networks.\n\nQ. In the Traditional Machine Learning, after a point of time the model starts to saturate — Why?\n\nAnswer:\n\nThe graph of data vs performanceis representative of the fact that models initially start performing better as more data is added to the training set. This is because the model is able to better generalize its parameters from the larger data set and thus the performance of the model increases. However, after a point of time the Traditional Machine Learning model starts to saturate, meaning that no matter how much data is added to the training set the performance of the model does not get any better. This is because the model has reached its theoretical limits and no more information can be gained from the additional data. Thus, after the model has reached its saturation point the performance of the model starts to degrade as more data is added.\n\nSaturated Model:\n\nA saturated model is a model that has been over-fitted to the data, meaning it contains too many parameters and is unable to accurately capture the underlying trends of the data. This can lead to a model that is too complex and not able to generalize to new data, resulting in poor predictions. This leads to extremely high-variancepredictors that are more sensitive to the noise than the true signal in the data, making them unreliable for predictions.\n\nQ. How Large Neural Network solve this saturation problem of Traditional Machine Learning models?\n\nLarge Neural networks are able to overcome the saturation problem faced by traditional machine learning algorithms by adding more parameters and layers to the model. By doing so, the model is able to learn more complex patterns and thus can more accurately fit the data. This allows the model to continue to improve even after the traditional machine learning algorithm has reached its saturation point. Additionally, neural networks also allow for more efficient training of the model by utilizing techniques such as batch normalization and weight decay, which help to reduce overfitting and improve the generalization of the model.\n\nBias in Machine Learning\n\nBias:\n\nBias is a parameter in a machine learning algorithm that allows the activation function to be shifted to the left or right.\n\nBias is used to adjust the output values of the model and influences how easy it is for a node to fire. Positive values of bias tend to produce higher output values while negative values of bias tend to produce lower output values.\n\nRole of Bias:\n\nBias allows the activation function to better fit the data.\n\nBias value is used to shift the entire sigmoid curve.\n\nBias does not interact with the actual input data.\n\nBias determines how easy it is to get a node to fire.\n\nLarge bias values lead to large positive outputs.\n\nNegative bias values lead to sigmoid outputs near to 0.\n\nSmall bias values depend entirely on the values of weights and inputs.\n\nQ. What problem may arise if the bias is very big or very small?\n\nAnswer:\n\nThe equation y=mx+b is a linear model that describes how a dependent variable (y) is related to an independent variable (x). The slope (m) of the line describes the relationship between x and y, while the intercept or bias (b) is the point where the line crosses the y-axis.\n\nDepending on the value of bias (b), the model can give priority to either the features or the bias.\n\nIf y=mx<b; then the model will depend on the bias. The model will give priority to the bias.\n\nIf y=mx>b; then the features will get priority rather than bias.\n\nIf the bias is very big, the model may be overly sensitive to the data and could result in inaccurate predictions. Overly sensitive data can lead to models that are overly complex, resulting in overfitting. This means that the model is too closely tailored to the training data and performs poorly on unseen data.\n\nOn the other hand, if the bias is very small, the model may be less sensitive to the data and could result in predictions that are too general. If a model is not sensitive enough to data, it can cause underfitting. This means that the model is not able to learn from the data and therefore unable to make accurate predictions.\n\nIn both cases, the model could be less accurate or biased.\n\nSo it is important to have a medium bias in order to ensure that the model is accurate and unbiased. Because having a medium bias in a linear model ensures that the model is neither too sensitive nor too general with regard to the data. This means that the model is more likely to make accurate and unbiased predictions.\n\nParameters & Hyperparameters\n\nParameters are values that are updated during the training process, they are updated by the algorithm, while hyperparameters are values that are set before training a model.\n\nParameters:\n\nParameters are internal to the model and whose value can be estimated from data.Parameters are required by the model when making predictions and are often not set manually by the practitioner.\n\nExamples of parameters include —\n\nWeight matrix of a model: A weight matrix is a set of numerical values used to represent the strength of the connections between nodes in a neural network.\n\nCoefficients in a Linear Regression:Coefficients in a linear regression are numerical values that determine the strength of the relationship between an independent variable and the dependent variable.\n\nDepth of a Decision Tree: The depth of a decision tree is the number of levels or layers of nodes present in the tree, which determines how complex the model is.\n\nHyperparameters:\n\nHyperparameters are external to the model and whose value cannot be estimated from data. They are set before training and determine how the network is trained.\n\nExamples of hyperparameters include —\n\nDropout: A technique used to reduce overfitting in neural networks by randomly disabling certain neurons during training.\n\nNumber of hidden layers: The number of layers between the input and output layers of a neural network.\n\nWeight initialization: The process of setting the initial values for weights in a neural network.\n\nActive function: An activation function is an non-linear transformation applied to the input of a neuron to produce an output.\n\nEpoch: An epoch is a single forward and backward pass of the entire training dataset.\n\nBatch size: The number of training samples used in one forward/backward pass.\n\nOptimizer: The process of adjusting the weights of a neural network to minimize a cost function.\n\nLearning rate: A parameter that controls how quickly a neural network learns. Loss function: A function that measures the performance of a neural network.\n\nIn short, what we assign in the model are called — hyperparameters.\n\nWhile what we don’t assign in the model but continuously update are called — parameters.\n\nDifference between Parameters and Hyper-parameters\n\nThe main differences between parameters and hyperparameters are:\n\nParameters are internal to the model and whose value can be estimated from data; whereas hyperparameters are external to the model and whose value cannot be estimated from data.\n\nParameters are required by the model when making predictions; whereas hyperparameters are set before training and determine how the network is trained.\n\nParameters are often not set manually by the practitioner; whereas hyperparameters are set manually by the practitioner.\n\nParameters are often saved as part of the learned model; whereas hyperparameters are not saved as part of the learned model.\n\nParameters define the skill of the model on a problem; whereas hyperparameters determine the network structure and used in processes to help estimate model parameters.\n\nHyperparameters of Traditional Machine Learning Models\n\nHyper-parameter Tuning is the process of selecting the best set of hyper-parameters for a given Machine Learning algorithm. It is done by testing different combinations of hyper-parameters and using techniques such as Random Search, Grid Search, Bayesian Optimizer, and Gradient-based to find the best combination.\n\nHere, after hyper-parametertuning the neural network is now more optimized and efficient. Dropout is used in the neural network in order to disable certain neurons. Dropout also reduced the chance of overfitting.\n\nHyper-parameter tuning can be used to adjust the structure of the network, such as the number of layers, the number of nodes per layer, the activation functions used in each layer, and the type of regularization used. By optimizing these parameters, the neural network can be tuned to better fit the data, resulting in improved performance.\n\nHyper-parameter Tuning Techniques:\n\nRandom Search:Random Search randomly chooses combinations of hyperparameters from searchable space (all possible combinations). An example of Random Search is randomly selecting the number of layers, neurons per layer, and learning rate.\n\nGrid Search:Grid Search tests all possible combinations of hyperparameters of given Machine Learning algorithm. An example of Grid Search is testing different combinations of layers, neurons per layer, and learning rate.\n\nBayesian Optimizer:Bayesian optimizer is based upon Bayes Rule and considers previously known knowledge to help narrow down the search space of good hyperparameter combinations. An example of Bayesian optimizer is using Bayesian optimization to find the best combination of learning rate, number of layers and number of neurons per layer.\n\nGradient-based:Gradient-based is a methodology to optimize several hyperparameters, based on the computation of the gradient of a machine learning model selection criterion with respect to the hyperparameters. An example of Gradient-based is using gradient descent to find the optimal combination of learning rate, number of layers and number of neurons per layer.\n\nQ. Why do we need hyper-parameter tuning?\n\nAnswer:\n\nBecause of the following reasons, we need hyper-parameter tuning —\n\nFinding the right balance between bias and variance to ensure high accuracy and good generalization.\n\nPreventing vanishing/exploding gradient problem by tweaking hyper-parameters like learning rate, activation function, and number of layers.\n\nOvercoming saddle points and local optima by adjusting learning rate and choosing appropriate optimizers.\n\nAvoiding no convergence by using adaptive learning rate optimizers like Adam or using decay in optimizers.\n\nAvoiding sigmoid and tanh activation function problems by limiting their usage to shallow networks.\n\nSpeeding up learning by getting the right set of hyper-parameters.\n\nDealing with umpteen combinations of all the hyper-parameters.\n\nAchieving more from less by making the best out of current architecture and slowly increasing complexity.\n\nSummary:\n\nTraditional Machine Learning relies on hand-crafted features and algorithms to detect patterns in data.\n\nSmall Neural Networks contain 1–2 hidden layers and are used to identify small patterns and relations in data.\n\nLarge Neural Networks contain 20–25 hidden layers, which enable them to detect complex patterns and relationships in large datasets.\n\nTraditional Machine Learning models start to saturate after a point of time due to reaching its theoretical limits.\n\nSaturated models lead to extremely high-variance predictors that are being pushed around by the noise more than the actual data.\n\nLarge Neural Networks are able to overcome the saturation problem by adding more parameters and layers, allowing the model to learn more complex patterns and thus improve its accuracy.\n\nBias is a parameter in a machine learning algorithm that allows the activation function to be shifted to the left or right.\n\nBias allows the activation function to better fit the data.\n\nIf the bias is very big, the model may be overly sensitive to the data and could result in inaccurate predictions.\n\nIf the bias is very small, the model may be less sensitive to the data and could result in predictions that are too general.\n\nIt is important to have a medium bias in order to ensure that the model is accurate and unbiased.\n\nParameters are internal values of a model that are estimated from data, while hyperparameters are external values that need to be set before training a model.\n\nParameters are used to make predictions, while hyperparameters determine how the model is trained.\n\nHyperparameter tuning is the process of finding the best set of hyperparameters for a given Machine Learning algorithm.\n\nTechniques used for hyperparameter tuning include Random Search, Grid Search, Bayesian Optimizer, and Gradient-based.\n\nHyperparameters are manually set by the practitioner, while parameters are automatically updated during training.\n\nSee you in the next Article. That’s it for now.\n\nIf you found this article interesting, helpful and if you learn something from this article, please follow, comment and leave feedback.\n\nIf you want to connect with me, here is my LinkedIn.", "url": "https://wpnews.pro/news/lesson-7-understanding-the-role-of-bias-parameters-and-hyperparameters-in", "canonical_source": "https://pub.towardsai.net/lesson-7-understanding-the-role-of-bias-parameters-and-hyperparameters-in-machine-learning-4cda48aaaba8?source=rss----98111c9905da---4", "published_at": "2026-09-04 04:41:03+00:00", "updated_at": "2026-09-04 05:22:06.856983+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/lesson-7-understanding-the-role-of-bias-parameters-and-hyperparameters-in", "markdown": "https://wpnews.pro/news/lesson-7-understanding-the-role-of-bias-parameters-and-hyperparameters-in.md", "text": "https://wpnews.pro/news/lesson-7-understanding-the-role-of-bias-parameters-and-hyperparameters-in.txt", "jsonld": "https://wpnews.pro/news/lesson-7-understanding-the-role-of-bias-parameters-and-hyperparameters-in.jsonld"}}