As machine learning is increasingly used in applications, machine learning algorithms have gained more scrutiny.
With larger data sets, various implementations, algorithms, and learning requirements, it has become even more complex to create and evaluate ML models since all those factors directly impact the overall accuracy and learning outcome of the model. This is further skewed by false assumptions, noise, and outliers.
Machine learning models cannot be a black box. The user needs to be fully aware of their data and algorithms to trust the outputs and outcomes. Any issues in the algorithm or polluted data set can negatively impact the ML model.
The main aim of ML/data science analysts is to reduce these errors in order to get more accurate results. In this topic, we are going to discuss bias and variance, Bias-variance trade-off, Underfitting, and Overfitting. But before starting, let’s first understand what errors in Machine learning are.
Type of Errors
There are two types of errors in Machine Learning.
Reducible Errors
These errors are those which can be eliminated or reduced to improve machine learning model performance. It includes Bias errors and Variance Errors
Irreducible Errors
These errors can not be reduced and they’re always there in the model.
What is Bias Error?
Bias is the simplifying assumptions made by a model to make the target function easier to learn.
Generally, linear algorithms have a high bias making them fast to learn and easier to understand but generally less flexible. In turn, they have lower predictive performance on complex problems that fail to meet the simplifying assumptions of the algorithms bias.
- Low Bias: Suggests fewer assumptions about the form of the target function.
- High-Bias: Suggests more assumptions about the form of the target function.
Examples of low-bias machine learning algorithms include Decision Trees, k-Nearest Neighbors, and Support Vector Machines.
Examples of high-bias machine learning algorithms include Linear Regression, Linear Discriminant Analysis, and Logistic Regression.
Ways to reduce High Bias:
High bias mainly occurs due to a simple model. Below are some ways to reduce the high bias:
- Increase the input features as the model is under-fitted.
- Decrease the regularization term.
- Use more complex models, such as including some polynomial features.
What is a Variance error?
Variance indicates the amount of variation in predictions when different training data are used. Simply put, variance tells you how much a random variable differs from its expected value. Ideally, the model should not change significantly from one training set to another. This means that the algorithm must have a good understanding of the hidden mappings between input and output variables. Variance error can be either a low variance or the high variance.
Low variance means that there is a small deviation in the prediction of the objective function as the training data set changes. At the same time, high variance indicates a large variation in objective function predictions as the training data set changes.
Bias-Variance Trade-Off
While building the machine learning model, it is really important to take care of bias and variance in order to avoid overfitting and underfitting in the model. If the model is very simple with fewer parameters, it may have low variance and high bias. Whereas, if the model has a large number of parameters, it will have high variance and low bias. So, it is required to make a balance between bias and variance errors, and this balance between the bias error and variance error is known as the Bias-Variance trade-off.
For an accurate prediction of the model, algorithms need a low variance and low bias. But this is not possible because bias and variance are related to each other:
- If we decrease the variance, it will increase the bias.
- If we decrease the bias, it will increase the variance.
The bias-Variance trade-off is a central issue in supervised learning. Ideally, we need a model that accurately captures the regularities in training data and simultaneously generalizes well with the unseen dataset. Unfortunately, doing this is not possible simultaneously. Because a high variance algorithm may perform well with training data, but it may lead to overfitting to noisy data. Whereas, high bias algorithm generates a much simple model that may not even capture important regularities in the data. So, we need to find a sweet spot between bias and variance to make an optimal model.
Hence, the Bias-Variance trade-off is about finding the sweet spot to make a balance between bias and variance errors.
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