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Bias and Variance in Machine Learning
A type of artificial intelligence called machine learning enables computers to analyze data and forecast outcomes. erroneous predictions, referred to as bias and variance, could be produced by an erroneous machine learning model. These errors are unavoidable in machine learning since there is always a minor gap between model predictions and actual forecasts. The primary goal of ML/data science analysts is to minimize these errors in order to obtain more accurate results.
Errors in Machine Learning?
In machine learning, an error is a measurement of how well an algorithm predicts a previously unknown dataset. Based on these errors, the machine learning model with the greatest performance on the given dataset is selected.
In machine learning, there are two primary categories of errors:
Reducible errors: These errors can be decreased to increase the accuracy of the model, and they are further characterized as bias or variance.
Irreducible errors: These mistakes will always exist in the model independent of the applied technique. The reason behind these mistakes is unknown factors whose value cannot be reduced.
What is Bias?
A machine learning model typically analyzes data, looks for patterns, and generates predictions. These patterns in the dataset are learned by the model during training, and it then applies them to test data to make predictions. When the model makes predictions, there is a discrepancy between the predicted values and the actual or expected values; this discrepancy is referred to as bias errors or bias-related mistakes. One definition of it is the incapacity of machine learning algorithms, like linear regression, to accurately depict the actual relationship between the data points. The goal function is easy to learn since bias arises from assumptions in the model, so every algorithm starts with some bias.
A model has either:
Low Bias: A model with low bias will make fewer assumptions regarding the form of the target function.
High Bias:A model with a high bias makes more assumptions, making it impossible to capture the crucial properties of our dataset.A model with a large bias cannot perform well with new data.
In general, a linear algorithm has a large bias since it enables rapid learning. The simpler the algorithm, the more bias it is likely to impose. In contrast, nonlinear algorithms frequently have little bias.
Low bias machine learning techniques include Decision Trees, k-Nearest Neighbours, and Support Vector Machines. Linear Regression, Linear Discriminant Analysis, and Logistic Regression are all examples of high-bias algorithms.
Ways to reduce High Bias:
- High bias is primarily caused by a very simplistic model. The following are some methods to reduce the high bias:
- Increase the input features if the model becomes underfitted.
- Reduce the regularization term.
- Use more sophisticated models, such as those using polynomial characteristics.
What is a Variance Error?
The variance would describe the amount of difference in the prediction if different training data were utilized. Simply said, variance describes how much a random variable deviates from its predicted value. Ideally, a model should not differ significantly from one training dataset to the next, which means the algorithm should be capable of grasping the hidden mapping between input and output variables. Variance errors can be either minimal or high variance.
Low variance indicates that the prediction of the target function varies only slightly as the training data set changes. Simultaneously, High variance shows a large variation in the prediction of the target function as the training dataset changes.
A model with a high variance learns quickly and performs well on the training dataset, but does not generalize well to the unknown dataset.Thus, such a model has high error rates on the testing dataset but performs well on the training dataset.When the model learns too much from the dataset because of high variation, this is known as overfitting.
A model with high variance has the following issues:
- A high variance model causes overfitting.
- Increase the model’s complexity.
Low variance machine learning approaches include linear regression, logistic regression, and linear discriminant analysis. At the same time, high variance algorithms include decision trees, Support Vector Machines, and K-nearest neighbours.
Ways to Reduce High Variance:
- Reduce the number of input characteristics or parameters as a model becomes overfitted.
- Do not employ a really sophisticated model.
- Increase your training data.
- Increase the Regularisation term.
Different Combinations of Bias-Variance
There are four different combinations of bias and variance.
Low-Bias, Low-Variance:An optimal machine learning model has both low bias and low variance. However, this is not practical.
Low-Bias, High-Variance: Model predictions are generally accurate and inconsistent, with low bias and high variance. This situation arises when the model learns with a lot of parameters, which causes overfitting.
High-Bias, Low-Variance: Predictions with low variance and high bias are generally correct but inconsistent. This situation arises when a model uses a small number of parameters or does not learn adequately with the training dataset. It causes issues with the model’s underfitting.
High-Bias, High-Variance: Predictions with high bias and variance are inconsistent and incorrect on average.
How to identify High variance or High Bias?
High variance can be recognized when the model has:
- The training error is low, however the test error is high.
A model with high bias can be detected as follows:
- High training error, and test error is nearly equivalent to training error.
Bias-Variance Trade-Off:
To reduce overfitting and underfitting, bias and variance are essential components to consider when creating a machine learning model. A basic model with few parameters may have low variance and high bias. In contrast, if the model contains a large number of parameters, it will have high variance but low bias. As a result, it is necessary to make a balance between bias and variance errors which is referred to as the Bias-Variance tradeoff.
One of the main concerns in supervised learning is the bias-variance trade-off. We want a model that can both generalize effectively to new datasets and faithfully represent the regularities in training data. Unfortunately, this cannot be done concurrently. Because while a high variance algorithm may perform well on training data, it may overfit on noisy data. In contrast, a high bias algorithm produces a considerably simpler model that may not even capture essential regularities in the data. To create an ideal model, we must discover the right balance of bias and variance.
As a result, the Bias-Variance trade-off is about determining the optimal balance of bias and variance errors.